Energy Expenditure Calculation Using Data From Multiple Devices

ABSTRACT

Aspects relate to athletic metric values from data received from different sources having different devices that employ different processes to calculate the values. Data may be received from a connected device that utilizes a first operating protocol, and be received by a device utilizing a second operating protocol. An activity metric may be identified from received data, and a determination may be made as to whether the activity metric is calculated using a best available data source. If it is determined that the received data represents a best available data source, the received data may be added to a metric database, the received data may be classified into an activity group, and an energy expenditure value may be calculated from the received data.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of, and priority to, U.S.Provisional Patent Application No. 62/099,851, entitled “ENERGYEXPENDITURE CALCULATION USING DATA FROM MULTIPLE DEVICES” filed Jan. 5,2015. The content of which is expressly incorporated herein by referencein its entirety for any and all non-limiting purposes.

BACKGROUND

While most people appreciate the importance of physical fitness, manyhave difficulty finding the motivation required to maintain a regularexercise program. Some people find it particularly difficult to maintainan exercise regimen that involves continuously repetitive motions, suchas running, walking and bicycling.

Additionally, individuals may view exercise as work or a chore and thus,separate it from enjoyable aspects of their daily lives. Often, thisclear separation between athletic activity and other activities reducesthe amount of motivation that an individual might have towardexercising. Further, athletic activity services and systems directedtoward encouraging individuals to engage in athletic activities mightalso be too focused on one or more particular activities while anindividual's interest are ignored. This may further decrease a user'sinterest in participating in athletic activities or using athleticactivity services and systems.

Many existing services and devices fail to provide accurate assessmentof the user's energy expenditure, such as caloric burn, during physicalactivity. Therefore, users are unaware of the benefits that certainactivities, which may include daily routines that are often not thoughtof as being a “workout”, are to their health. Existing devices forallowing users to monitor their energy expenditure often suffer from oneor more deficiencies, including: cumbersome collection systems,inaccurate measurements that are beyond an acceptable threshold,unacceptable latency in reporting the values, erroneous classificationof activities based upon detected motions of the user, failure toaccount for deviations between different users, improperly includingrepetitive behavior as being classified as a specific activity, such asfor example, running and/or walking, relatively high power consumption,and/or a combination of these or other deficiencies.

Therefore, improved systems and methods to address at least one or moreof these shortcomings in the art are desired.

BRIEF SUMMARY

The following presents a simplified summary of the present disclosure inorder to provide a basic understanding of some aspects of the invention.This summary is not an extensive overview of the invention. It is notintended to identify key or critical elements of the invention or todelineate the scope of the invention. The following summary merelypresents some concepts of the invention in a simplified form as aprelude to the more detailed description provided below.

Illustrative embodiments may relate to a system, method, apparatus, andcomputer-readable media configured for receiving data from a connecteddevice that utilizes a first operating protocol. In one embodiment, thedata may be received by a processor that utilizes a second operatingprotocol. Further, an activity metric may be identified from thereceived data. In certain embodiments, the identified activity metricmay be compared to a metric database to determine whether a bestavailable source of data is used to calculate the activity metric. Ifthe activity metric is calculated from a best available data source, thereceived data may be added to a metric database, the received data maybe classified into an activity group, and an energy expenditure for auser may be calculated from one or more stored activity metrics.

In one embodiment, an activity being performed by a user is identifiedbased upon the user input of an activity classification.

Another illustrative embodiment may relate to a system, method,apparatus, and computer-readable media configured for receiving datafrom a connected device that utilizes a first operating protocol. In oneembodiment, the data may be received by a processor that utilizes asecond operating protocol. Further, an activity metric may be identifiedfrom the received data. In certain embodiments, the identified activitymetric may be compared to a metric database to determine whether thebest available source of data is used to calculate the activity metric.If the activity metric is calculated from the best available datasource, the received data may be added to the metric database, thereceived data may be classified into an activity group, a confidenceweighting may be associated with the activity metric, an energyexpenditure value for a user may be calculated from one or more storedactivity metrics, and a confidence value may be calculated andassociated with the calculated energy expenditure value.

Yet another illustrative embodiment may relate to a system, method,apparatus, and computer-readable media configured for calculating afinal cumulative athletic metric based upon a user's movements during afirst time frame. In certain embodiments, the first time frame maycomprise a first and a second time period. Further, in certainembodiments, first and second values may be received from first andsecond sources that represent athletic metrics resulting from processesconducted on first and second devices. The first value may be associatedwith a modification scalar based on the first source and the firstdevice. Further, a normalizing factor may be used to normalize data fromthe first source and the second source. Additionally, a cumulativenormalized athletic metric may be calculated using an adjusted firstvalue and an adjusted second value.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example system that may be configured to providepersonal training and/or obtain data from the physical movements of auser in accordance with example embodiments;

FIG. 2 illustrates an example computer device that may be part of or incommunication with the system of FIG. 1.

FIG. 3 shows an illustrative sensor assembly that may be worn by a userin accordance with example embodiments;

FIG. 4 shows another example sensor assembly that may be worn by a userin accordance with example embodiments;

FIG. 5 shows illustrative locations for sensory input which may includephysical sensors located on/in a user's clothing and/or be based uponidentification of relationships between two moving body parts of theuser;

FIG. 6 shows an example flowchart that may be implemented to calculatemetrics, including energy expenditure and speed.

FIGS. 7A and 7B show example flowcharts that may be utilized to classifyactivity and/or calculate energy expenditure values of an individual.

FIG. 8A illustrates an example flow diagram which may be used incalculating a cumulative energy expenditure estimate based on energyexpenditure estimates obtained by two or more different processes, whichmay conducted on entirely different devices incapable of communicatingwith each other, either directly or indirectly. FIG. 8B shows a flowdiagram of calculating a cumulative normalized athletic metric inaccordance with one embodiment.

FIG. 9 schematically-depicts one implementation of a process forcalculating an energy expenditure of a user from data received from oneor more sources.

FIG. 10 schematically-depicts another implementation of a process forcalculating an energy expenditure of a user from data received from oneor more sources.

FIG. 11 schematically-depicts an implementation of a process forcalculation of a confidence value to be associated with a calculatedenergy expenditure value.

FIG. 12 is a flowchart diagram of a process for publishing activityinformation to a social feed.

FIG. 13 is a flow diagram depicting an example implementation ofutilizing two different metrics derived from different processes atdifferent sources.

DETAILED DESCRIPTION

Aspects of this disclosure involve obtaining, storing, and/or processingathletic data relating to the physical movements of an athlete. Theathletic data may be actively or passively sensed and/or stored in oneor more non-transitory storage mediums. Still further aspects relate tousing athletic data to generate an output, such as for example,calculated athletic attributes, feedback signals to provide guidance,and/or other information. These and other aspects will be discussed inthe context of the following illustrative examples of a personaltraining system.

In the following description of the various embodiments, reference ismade to the accompanying drawings, which form a part hereof, and inwhich is shown by way of illustration various embodiments in whichaspects of the disclosure may be practiced. It is to be understood thatother embodiments may be utilized and structural and functionalmodifications may be made without departing from the scope and spirit ofthe present disclosure. Further, headings within this disclosure shouldnot be considered as limiting aspects of the disclosure and the exampleembodiments are not limited to the example headings.

I. Example Personal Training System

A. Illustrative Networks

Aspects of this disclosure relate to systems and methods that may beutilized across a plurality of networks. In this regard, certainembodiments may be configured to adapt to dynamic network environments.Further embodiments may be operable in differing discrete networkenvironments. FIG. 1 illustrates an example of a personal trainingsystem 100 in accordance with example embodiments. Example system 100may include one or more interconnected networks, such as theillustrative body area network (BAN) 102, local area network (LAN) 104,and wide area network (WAN) 106. As shown in FIG. 1 (and describedthroughout this disclosure), one or more networks (e.g., BAN 102, LAN104, and/or WAN 106), may overlap or otherwise be inclusive of eachother. Those skilled in the art will appreciate that the illustrativenetworks 102-106 are logical networks that may each comprise one or moredifferent communication protocols and/or network architectures and yetmay be configured to have gateways to each other or other networks. Forexample, each of BAN 102, LAN 104 and/or WAN 106 may be operativelyconnected to the same physical network architecture, such as cellularnetwork architecture 108 and/or WAN architecture 110. For example,portable electronic device 112, which may be considered a component ofboth BAN 102 and LAN 104, may comprise a network adapter or networkinterface card (NIC) configured to translate data and control signalsinto and from network messages according to one or more communicationprotocols, such as the Transmission Control Protocol (TCP), the InternetProtocol (IP), and the User Datagram Protocol (UDP) through one or moreof architectures 108 and/or 110. These protocols are well known in theart, and thus will not be discussed here in more detail.

Network architectures 108 and 110 may include one or more informationdistribution network(s), of any type(s) or topology(s), alone or incombination(s), such as for example, cable, fiber, satellite, telephone,cellular, wireless, etc. and as such, may be variously configured suchas having one or more wired or wireless communication channels(including but not limited to: WiFi®, Bluetooth®, Near-FieldCommunication (NFC) and/or ANT technologies). Thus, any device within anetwork of FIG. 1, (such as portable electronic device 112 or any otherdevice described herein) may be considered inclusive to one or more ofthe different logical networks 102-106. With the foregoing in mind,example components of an illustrative BAN and LAN (which may be coupledto WAN 106) will be described.

1. Example Local Area Network

LAN 104 may include one or more electronic devices, such as for example,computer device 114. Computer device 114, or any other component ofsystem 100, may comprise a mobile terminal, such as a telephone, musicplayer, tablet, netbook or any portable device. In other embodiments,computer device 114 may comprise a media player or recorder, desktopcomputer, server(s), a gaming console, such as for example, a Microsoft®XBOX, Sony® Playstation, and/or a Nintendo® Wii gaming consoles. Thoseskilled in the art will appreciate that these are merely example devicesfor descriptive purposes and this disclosure is not limited to anyconsole or computing device.

Those skilled in the art will appreciate that the design and structureof computer device 114 may vary depending on several factors, such asits intended purpose. One example implementation of computer device 114is provided in FIG. 2, which illustrates a block diagram of computingdevice 200. Those skilled in the art will appreciate that the disclosureof FIG. 2 may be applicable to any device disclosed herein. Device 200may include one or more processors, such as processor 202-1 and 202-2(generally referred to herein as “processors 202” or “processor 202”).Processors 202 may communicate with each other or other components viaan interconnection network or bus 204. Processor 202 may include one ormore processing cores, such as cores 206-1 and 206-2 (referred to hereinas “cores 206” or more generally as “core 206”), which may beimplemented on a single integrated circuit (IC) chip.

Cores 206 may comprise a shared cache 208 and/or a private cache (e.g.,caches 210-1 and 210-2, respectively). One or more caches 208/210 maylocally cache data stored in a system memory, such as memory 212, forfaster access by components of the processor 202. Memory 212 may be incommunication with the processors 202 via a chipset 216. Cache 208 maybe part of system memory 212 in certain embodiments. Memory 212 mayinclude, but is not limited to, random access memory (RAM), read onlymemory (ROM), and include one or more of solid-state memory, optical ormagnetic storage, and/or any other medium that can be used to storeelectronic information. Yet other embodiments may omit system memory212.

System 200 may include one or more I/O devices (e.g., I/O devices 214-1through 214-3, each generally referred to as I/O device 214). I/O datafrom one or more I/O devices 214 may be stored at one or more caches208, 210 and/or system memory 212. Each of I/O devices 214 may bepermanently or temporarily configured to be in operative communicationwith a component of system 100 using any physical or wirelesscommunication protocol.

Returning to FIG. 1, four example I/O devices (shown as elements116-122) are shown as being in communication with computer device 114.Those skilled in the art will appreciate that one or more of devices116-122 may be stand-alone devices or may be associated with anotherdevice besides computer device 114. For example, one or more I/O devicesmay be associated with or interact with a component of BAN 102 and/orWAN 106. I/O devices 116-122 may include, but are not limited toathletic data acquisition units, such as for example, sensors. One ormore I/O devices may be configured to sense, detect, and/or measure anathletic parameter from a user, such as user 124. Examples include, butare not limited to: an accelerometer, a gyroscope, alocation-determining device (e.g., GPS), light (including non-visiblelight) sensor, temperature sensor (including ambient temperature and/orbody temperature), sleep pattern sensors, heart rate monitor,image-capturing sensor, moisture sensor, force sensor, compass, angularrate sensor, and/or combinations thereof among others.

In further embodiments, I/O devices 116-122 may be used to provide anoutput (e.g., audible, visual, or tactile cue) and/or receive an input,such as a user input from athlete 124. Example uses for theseillustrative I/O devices are provided below, however, those skilled inthe art will appreciate that such discussions are merely descriptive ofsome of the many options within the scope of this disclosure. Further,reference to any data acquisition unit, I/O device, or sensor is to beinterpreted disclosing an embodiment that may have one or more I/Odevice, data acquisition unit, and/or sensor disclosed herein or knownin the art (either individually or in combination).

Information from one or more devices (across one or more networks) maybe used to provide (or be utilized in the formation of) a variety ofdifferent parameters, metrics or physiological characteristics includingbut not limited to: motion parameters, such as speed, acceleration,distance, steps taken, direction, relative movement of certain bodyportions or objects to others, or other motion parameters which may beexpressed as angular rates, rectilinear rates or combinations thereof,physiological parameters, such as calories, heart rate, sweat detection,effort, oxygen consumed, oxygen kinetics, and other metrics which mayfall within one or more categories, such as: pressure, impact forces,information regarding the athlete, such as height, weight, age,demographic information and combinations thereof.

System 100 may be configured to transmit and/or receive athletic data,including the parameters, metrics, or physiological characteristicscollected within system 100 or otherwise provided to system 100. As oneexample, WAN 106 may comprise server 111. Server 111 may have one ormore components of system 200 of FIG. 2. In one embodiment, server 111comprises at least a processor and a memory, such as processor 206 andmemory 212. Server 111 may be configured to store computer-executableinstructions on a non-transitory computer-readable medium. Theinstructions may comprise athletic data, such as raw or processed datacollected within system 100. System 100 may be configured to transmitdata, such as energy expenditure points, to a social networking websiteor host such a site. Server 111 may be utilized to permit one or moreusers to access and/or compare athletic data. As such, server 111 may beconfigured to transmit and/or receive notifications based upon athleticdata or other information.

Returning to LAN 104, computer device 114 is shown in operativecommunication with a display device 116, an image-capturing device 118,sensor 120 and exercise device 122, which are discussed in turn belowwith reference to example embodiments. In one embodiment, display device116 may provide audio-visual cues to athlete 124 to perform a specificathletic movement. The audio-visual cues may be provided in response tocomputer-executable instruction executed on computer device 114 or anyother device, including a device of BAN 102 and/or WAN. Display device116 may be a touchscreen device or otherwise configured to receive auser-input.

In one embodiment, data may be obtained from image-capturing device 118and/or other sensors, such as sensor 120, which may be used to detect(and/or measure) athletic parameters, either alone or in combinationwith other devices, or stored information. Image-capturing device 118and/or sensor 120 may comprise a transceiver device. In one embodimentsensor 128 may comprise an infrared (IR), electromagnetic (EM) oracoustic transceiver. For example, image-capturing device 118, and/orsensor 120 may transmit waveforms into the environment, includingtowards the direction of athlete 124 and receive a “reflection” orotherwise detect alterations of those released waveforms. Those skilledin the art will readily appreciate that signals corresponding to amultitude of different data spectrums may be utilized in accordance withvarious embodiments. In this regard, devices 118 and/or 120 may detectwaveforms emitted from external sources (e.g., not system 100). Forexample, devices 118 and/or 120 may detect heat being emitted from user124 and/or the surrounding environment. Thus, image-capturing device 126and/or sensor 128 may comprise one or more thermal imaging devices. Inone embodiment, image-capturing device 126 and/or sensor 128 maycomprise an IR device configured to perform range phenomenology.

In one embodiment, exercise device 122 may be any device configurable topermit or facilitate the athlete 124 performing a physical movement,such as for example a treadmill, step machine, etc. There is norequirement that the device be stationary. In this regard, wirelesstechnologies permit portable devices to be utilized, thus a bicycle orother mobile exercising device may be utilized in accordance withcertain embodiments. Those skilled in the art will appreciate thatequipment 122 may be or comprise an interface for receiving anelectronic device containing athletic data performed remotely fromcomputer device 114. For example, a user may use a sporting device(described below in relation to BAN 102) and upon returning home or thelocation of equipment 122, download athletic data into element 122 orany other device of system 100. Any I/O device disclosed herein may beconfigured to receive activity data.

2. Body Area Network

BAN 102 may include two or more devices configured to receive, transmit,or otherwise facilitate the collection of athletic data (includingpassive devices). Exemplary devices may include one or more dataacquisition units, sensors, or devices known in the art or disclosedherein, including but not limited to I/O devices 116-122. Two or morecomponents of BAN 102 may communicate directly, yet in otherembodiments, communication may be conducted via a third device, whichmay be part of BAN 102, LAN 104, and/or WAN 106. One or more componentsof LAN 104 or WAN 106 may form part of BAN 102. In certainimplementations, whether a device, such as portable device 112, is partof BAN 102, LAN 104, and/or WAN 106, may depend on the athlete'sproximity to an access point to permit communication with mobilecellular network architecture 108 and/or WAN architecture 110. Useractivity and/or preference may also influence whether one or morecomponents are utilized as part of BAN 102. Example embodiments areprovided below.

User 124 may be associated with (e.g., possess, carry, wear, and/orinteract with) any number of devices, such as portable device 112,shoe-mounted device 126, wrist-worn device 128 and/or a sensinglocation, such as sensing location 130, which may comprise a physicaldevice or a location that is used to collect information. One or moredevices 112, 126, 128, and/or 130 may not be specially designed forfitness or athletic purposes. Indeed, aspects of this disclosure relateto utilizing data from a plurality of devices, some of which are notfitness devices, to collect, detect, and/or measure athletic data. Incertain embodiments, one or more devices of BAN 102 (or any othernetwork) may comprise a fitness or sporting device that is specificallydesigned for a particular sporting use. As used herein, the term“sporting device” includes any physical object that may be used orimplicated during a specific sport or fitness activity. Exemplarysporting devices may include, but are not limited to: golf balls,basketballs, baseballs, soccer balls, footballs, powerballs, hockeypucks, weights, bats, clubs, sticks, paddles, mats, and combinationsthereof. In further embodiments, exemplary fitness devices may includeobjects within a sporting environment where a specific sport occurs,including the environment itself, such as a goal net, hoop, backboard,portions of a field, such as a midline, outer boundary marker, base, andcombinations thereof.

In this regard, those skilled in the art will appreciate that one ormore sporting devices may also be part of (or form) a structure andvice-versa, a structure may comprise one or more sporting devices or beconfigured to interact with a sporting device. For example, a firststructure may comprise a basketball hoop and a backboard, which may beremovable and replaced with a goal post. In this regard, one or moresporting devices may comprise one or more sensors, such as one or moreof the sensors discussed above in relation to FIGS. 1-3, that mayprovide information utilized, either independently or in conjunctionwith other sensors, such as one or more sensors associated with one ormore structures. For example, a backboard may comprise a first sensorconfigured to measure a force and a direction of the force by abasketball upon the backboard and the hoop may comprise a second sensorto detect a force. Similarly, a golf club may comprise a first sensorconfigured to detect grip attributes on the shaft and a second sensorconfigured to measure impact with a golf ball.

Looking to the illustrative portable device 112, it may be amulti-purpose electronic device, that for example, includes a telephoneor digital music player, including an IPOD®, IPAD®, or iPhone®, branddevices available from Apple, Inc. of Cupertino, Calif. or Zune® orMicrosoft® Windows devices available from Microsoft of Redmond, Wash. Asknown in the art, digital media players can serve as an output device,input device, and/or storage device for a computer. Device 112 may beconfigured as an input device for receiving raw or processed datacollected from one or more devices in BAN 102, LAN 104, or WAN 106. Inone or more embodiments, portable device 112 may comprise one or morecomponents of computer device 114. For example, portable device 112 maybe include a display 116, image-capturing device 118, and/or one or moredata acquisition devices, such as any of the I/O devices 116-122discussed above, with or without additional components, so as tocomprise a mobile terminal.

a. Illustrative Apparel/Accessory Sensors

In certain embodiments, I/O devices may be formed within or otherwiseassociated with user's 124 clothing or accessories, including a watch,armband, wristband, necklace, shirt, shoe, or the like. These devicesmay be configured to monitor athletic movements of a user. It is to beunderstood that they may detect athletic movement during user's 124interactions with computer device 114 and/or operate independently ofcomputer device 114 (or any other device disclosed herein). For example,one or more devices in BAN 102 may be configured to function as anall-day activity monitor that measures activity regardless of the user'sproximity or interactions with computer device 114. It is to be furtherunderstood that the sensory system 302 shown in FIG. 3 and the deviceassembly 400 shown in FIG. 4, each of which are described in thefollowing paragraphs, are merely illustrative examples.

i. Shoe-Mounted Device

In certain embodiments, device 126 shown in FIG. 1, may comprisefootwear which may include one or more sensors, including but notlimited to those disclosed herein and/or known in the art. FIG. 3illustrates one example embodiment of a sensor system 302 providing oneor more sensor assemblies 304. Assembly 304 may comprise one or moresensors, such as for example, an accelerometer, gyroscope,location-determining components, force sensors and/or or any othersensor disclosed herein or known in the art. In the illustratedembodiment, assembly 304 incorporates a plurality of sensors, which mayinclude force-sensitive resistor (FSR) sensors 306; however, othersensor(s) may be utilized. Port 308 may be positioned within a solestructure 309 of a shoe, and is generally configured for communicationwith one or more electronic devices. Port 308 may optionally be providedto be in communication with an electronic module 310, and the solestructure 309 may optionally include a housing 311 or other structure toreceive the module 310. The sensor system 302 may also include aplurality of leads 312 connecting the FSR sensors 306 to the port 308,to enable communication with the module 310 and/or another electronicdevice through the port 308. Module 310 may be contained within a wellor cavity in a sole structure of a shoe, and the housing 311 may bepositioned within the well or cavity. In one embodiment, at least onegyroscope and at least one accelerometer are provided within a singlehousing, such as module 310 and/or housing 311. In at least a furtherembodiment, one or more sensors are provided that, when operational, areconfigured to provide directional information and angular rate data. Theport 308 and the module 310 include complementary interfaces 314, 316for connection and communication.

In certain embodiments, at least one force-sensitive resistor 306 shownin FIG. 3 may contain first and second electrodes or electrical contacts318, 320 and a force-sensitive resistive material 322 disposed betweenthe electrodes 318, 320 to electrically connect the electrodes 318, 320together. When pressure is applied to the force-sensitive material 322,the resistivity and/or conductivity of the force-sensitive material 322changes, which changes the electrical potential between the electrodes318, 320. The change in resistance can be detected by the sensor system302 to detect the force applied on the sensor 316. The force-sensitiveresistive material 322 may change its resistance under pressure in avariety of ways. For example, the force-sensitive material 322 may havean internal resistance that decreases when the material is compressed.Further embodiments may utilize “volume-based resistance”, which may beimplemented through “smart materials.” As another example, the material322 may change the resistance by changing the degree ofsurface-to-surface contact, such as between two pieces of the forcesensitive material 322 or between the force sensitive material 322 andone or both electrodes 318, 320. In some circumstances, this type offorce-sensitive resistive behavior may be described as “contact-basedresistance.”

ii. Wrist-Worn Device

As shown in FIG. 4, device 400 (which may resemble or comprise sensorydevice 128 shown in FIG. 1), may be configured to be worn by user 124,such as around a wrist, arm, ankle, neck or the like. Device 400 mayinclude an input mechanism, such as a depressible input button 402configured to be used during operation of the device 400. The inputbutton 402 may be operably connected to a controller 404 and/or anyother electronic components, such as one or more of the elementsdiscussed in relation to computer device 114 shown in FIG. 1. Controller404 may be embedded or otherwise part of housing 406. Housing 406 may beformed of one or more materials, including elastomeric components andcomprise one or more displays, such as display 408. The display may beconsidered an illuminable portion of the device 400. The display 408 mayinclude a series of individual lighting elements or light members suchas LED lights 410. The lights may be formed in an array and operablyconnected to the controller 404. Device 400 may include an indicatorsystem 412, which may also be considered a portion or component of theoverall display 408. Indicator system 412 can operate and illuminate inconjunction with the display 408 (which may have pixel member 414) orcompletely separate from the display 408. The indicator system 412 mayalso include a plurality of additional lighting elements or lightmembers, which may also take the form of LED lights in an exemplaryembodiment. In certain embodiments, indicator system may provide avisual indication of goals, such as by illuminating a portion oflighting members of indicator system 412 to represent accomplishmenttowards one or more goals. Device 400 may be configured to display dataexpressed in terms of activity points or currency earned by the userbased on the activity of the user, either through display 408 and/orindicator system 412.

A fastening mechanism 416 can be disengaged wherein the device 400 canbe positioned around a wrist or portion of the user 124 and thefastening mechanism 416 can be subsequently placed in an engagedposition. In one embodiment, fastening mechanism 416 may comprise aninterface, including but not limited to a USB port, for operativeinteraction with computer device 114 and/or devices, such as devices 120and/or 112. In certain embodiments, fastening member may comprise one ormore magnets. In one embodiment, fastening member may be devoid ofmoving parts and rely entirely on magnetic forces.

In certain embodiments, device 400 may comprise a sensor assembly (notshown in FIG. 4). The sensor assembly may comprise a plurality ofdifferent sensors, including those disclosed herein and/or known in theart. In an example embodiment, the sensor assembly may comprise orpermit operative connection to any sensor disclosed herein or known inthe art. Device 400 and or its sensor assembly may be configured toreceive data obtained from one or more external sensors.

iii. Apparel and/or Body Location Sensing

Element 130 of FIG. 1 shows an example sensory location which may beassociated with a physical apparatus, such as a sensor, data acquisitionunit, or other device. Yet in other embodiments, it may be a specificlocation of a body portion or region that is monitored, such as via animage capturing device (e.g., image capturing device 118). In certainembodiments, element 130 may comprise a sensor, such that elements 130 aand 130 b may be sensors integrated into apparel, such as athleticclothing. Such sensors may be placed at any desired location of the bodyof user 124. Sensors 130 a/b may communicate (e.g., wirelessly) with oneor more devices (including other sensors) of BAN 102, LAN 104, and/orWAN 106. In certain embodiments, passive sensing surfaces may reflectwaveforms, such as infrared light, emitted by image-capturing device 118and/or sensor 120. In one embodiment, passive sensors located on user's124 apparel may comprise generally spherical structures made of glass orother transparent or translucent surfaces which may reflect waveforms.Different classes of apparel may be utilized in which a given class ofapparel has specific sensors configured to be located proximate to aspecific portion of the user's 124 body when properly worn. For example,golf apparel may include one or more sensors positioned on the apparelin a first configuration and yet soccer apparel may include one or moresensors positioned on apparel in a second configuration.

FIG. 5 shows illustrative locations for sensory input (see, e.g.,sensory locations 130 a-130 o). In this regard, sensors may be physicalsensors located on/in a user's clothing, yet in other embodiments,sensor locations 130 a-130 o may be based upon identification ofrelationships between two moving body parts. For example, sensorlocation 130 a may be determined by identifying motions of user 124 withan image-capturing device, such as image-capturing device 118. Thus, incertain embodiments, a sensor may not physically be located at aspecific location (such as one or more of sensor locations 130 a-130 o),but is configured to sense properties of that location, such as withimage-capturing device 118 or other sensor data gathered from otherlocations. In this regard, the overall shape or portion of a user's bodymay permit identification of certain body parts. Regardless of whetheran image-capturing device is utilized and/or a physical sensor locatedon the user 124, and/or using data from other devices, (such as sensorysystem 302), device assembly 400 and/or any other device or sensordisclosed herein or known in the art is utilized, the sensors may sensea current location of a body part and/or track movement of the bodypart. In one embodiment, sensory data relating to location 130 m may beutilized in a determination of the user's center of gravity (a.k.a,center of mass). For example, relationships between location 130 a andlocation(s) 130 f/130 l with respect to one or more of location(s) 130m-130 o may be utilized to determine if a user's center of gravity hasbeen elevated along the vertical axis (such as during a jump) or if auser is attempting to “fake” a jump by bending and flexing their knees.In one embodiment, sensor location 1306 n may be located at about thesternum of user 124. Likewise, sensor location 130 o may be locatedapproximate to the naval of user 124. In certain embodiments, data fromsensor locations 130 m-130 o may be utilized (alone or in combinationwith other data) to determine the center of gravity for user 124. Infurther embodiments, relationships between multiple sensor locations,such as sensors 130 m-130 o, may be utilized in determining orientationof the user 124 and/or rotational forces, such as twisting of user's 124torso. Further, one or more locations, such as location(s), may beutilized as (or approximate) a center of moment location. For example,in one embodiment, one or more of location(s) 130 m-130 o may serve as apoint for a center of moment location of user 124. In anotherembodiment, one or more locations may serve as a center of moment ofspecific body parts or regions.

Example Metrics Calculations

Aspects of this disclosure relate to systems and methods that may beutilized to calculate one or more activity metrics of an athlete,including but not limited to energy expenditure, speed, distance, pace,power, and/or others. The calculations may be performed in real time,such that the user may obtain real-time feedback during one or moreactivities. In certain embodiments, all calculations for a plurality ofmetrics may be estimated using a same set of attributes, or a sub-set ofattributes from a common group of attributes, and the like. In oneembodiment, a calculation of energy expenditure may be performed on afirst set of attributes and without classifying the activity beingperformed by the athlete, such as being walking, running, playing aspecific sport, or conducting a specific activity. In one embodiment,determinations of energy expenditure are done without any activity typetemplates, such that as the energy expenditure may be calculated fromsensor data and/or derivatives thereof, without classifying the activitytype. For example, energy expenditure may be calculated in accordancewith certain embodiments using the same set of attributes regardless ofwhether the athlete is performing a first activity or a second activity,such as for example, walking or playing soccer.

In certain implementations, calculations of energy expenditurecalculated may be performed using a first set of attributes and anothermetric, such as for example, speed may be determined from the same setof attributes or a subset of the same attributes. In one embodiment,determination of a plurality of metrics may be conducted using aselection of core attributes. In one example, this attribute calculationmay be used to estimate energy expenditure and/or a speed of walkingand/or running of the user. In one example, an energy expenditure and/orspeed of walking and/or running may be estimated using a same set ofattributes, or a sub-set of attributes from a common group ofattributes, and the like.

The systems and methods described herein may compare calculatedattributes from activity data (real-time activity data, and the like) toone or more models wherein the one or more models may not include datacaptured for the activity type that the athlete performed (and may notbe categorized, such as for energy expenditure calculations). In thisway, the one or more models may be agnostic to the specific activitybeing performed by a user. For example, an activity device may receiveinformation from a user performing a basketball activity and at leastone model may not contain any data from basketball activities.

As an example of calculating multiple metrics, systems and methods maybe implemented to determine whether to calculate speed for one or moretime windows of data. Certain aspects of this disclosure relate todeterminations of speed or distance that comprises categorizing athleticdata. As discussed above, however, other aspects relate to calculatingenergy expenditure values without categorizing the athletic data intoactivity types (walking, running, basketball, sprinting, soccer,football, etc.), however, categorizing at least a portion of the samedata utilized to calculate energy expenditure for the calculation ofother metrics, such as for example, speed and/or distance is within thescope of this disclosure. In one implementation, speed (or anothermetric) may be determined from at least a portion of data derived fromthe determination of energy expenditure values. In accordance withcertain embodiments, the attributes are calculated on a single device,such as any device disclosed herein or known in the art. In oneembodiment, the attributes and calculation of the metrics are calculatedon a single device. In one such example, a device configured to be wornon an appendage of a user may be configured to receive sensor data andcalculate the attributes and a plurality of metrics from the attributes.In one embodiment, the single device comprises at least one sensorconfigured capture data utilized to calculate at least one attribute. Inaccordance with certain embodiments, the attributes are calculated fromone or more sensors located on the single device.

Example Energy Expenditure Calculations

One or more of the systems and methods described herein may calculate anestimate of energy expenditure. As such, one or more of the systems andmethods described herein may implement at least one of the components ofFIG. 6. Accordingly, in one example, FIG. 6 describes components thatmay be used to calculate energy expenditure from unprocessed sensordata, otherwise referred to as raw sensor data. In one configuration, adevice, such as device 112, 126, 128, 130, and/or 400, may capture dataassociated with one or more activities being performed by a user. Saiddevice may comprise one or more sensors, including, but not limited to:an accelerometer, a gyroscope, a location-determining device (e.g.,GPS), light sensor, temperature sensor (including ambient temperatureand/or body temperature), heart rate monitor, image-capturing sensor,moisture sensor and/or combinations thereof. This captured activity datamay, in turn, be used to calculate one or more energy expenditure valuesassociated with the user.

In one implementation, sensor data may be received from a first device,such as device 112, 126, 128, 130, and/or 400, wherein said first devicemay utilize a first operating protocol. This first operating protocolmay include one or more processes configured to control the operationof/one or more tasks executed by, the first device. In one example, anoperating protocol, such as the first operating protocol, may bereferred to as an operating system, or program code, among others.Further, the first device may be associated with a first manufacturer,among others. In one example, data received from the first device may bereceived by a second device that utilizes a second operating protocol.Accordingly, said second device may include one or more of devices 112,126, 128, 130, and/or 400, and such that the second operating protocolof said second device is not fully compatible with the first operatingprotocol. Those of ordinary skill in the art will recognize variousincompatibilities that may arise with regard to different operatingprotocols. For example, data may be processed, manipulated, and/orformatted by the first operating protocol using a first syntax ormethodology, and may be processed, manipulated, and/or formatted by thesecond operating protocol using a second syntax or methodology. In thisway, the first operating protocol and the second operating protocol maygenerally be described as incompatible with one another, or not fullycompatible with one another, among others. Accordingly, in oneimplementation, the systems and methods described herein may be utilizedto calculate one or more energy expenditure values using data receivedfrom a first device that utilizes a first operating protocol, andreceived by a second device that utilizes a second operating protocol.

In one implementation, an estimation of the volume of oxygen consumed bya person may be used to calculate an effective metabolic equivalent, oran estimation of energy expenditure, by said person. For example, in oneembodiment, a liter of oxygen consumed by a person may be associatedwith an energy expenditure of approximately 5 kilocalories (5 kcal).Additionally or alternatively, those of ordinary skill in the art willrecognize that various alternative methodologies exist for calculationof energy expenditure based upon oxygen consumption by a person.

In one embodiment, oxygen consumption by a person may be measured as avolume of oxygen per unit mass, such as per kilogram (VO₂/kg). In oneimplementation, the systems and methods described herein may utilizevalues, such as stored as computer-executable instructions on one ormore non-transitory computer-readable mediums related to actual and/orestimated oxygen consumption associated with specific activities. Incertain embodiments, the values may be actual data or derived fromactual data collected from one or more individuals performing one ormore specific activities, and otherwise referred to as training data.For example, the systems and methods described herein may utilizetraining data related to oxygen consumption by one or more individualsperforming activities including, among others, playing basketball,playing soccer, playing tennis, walking, jogging, running, andsprinting, sitting, squatting, and/or combinations thereof. Those ofordinary skill in the art will recognize various testing procedures thatmay be utilized to monitor oxygen consumption while an individual isperforming one or more prescribed activities. Additionally, it will bereadily understood to those of ordinary skill in the art that multipleoxygen consumption data points may be collected during an activity.Furthermore, one or more data manipulation processes may be performed onsaid collected data points to calculate, among others, an average oxygenconsumption during a specific activity, and based on a recorded mass(e.g., measured in kilograms, and the like), and/or individual or classspecific information (e.g., sex, weigh, height, age, percent body fat,among others).

In one implementation, training data may be recorded for one or moreindividuals performing one or more specific activities, wherein saidtraining data includes information related to a volume of oxygenconsumed at one or more time points during the one or more specificactivities, and information related to individual and or class-specificinformation (e.g., the mass of the individuals performing the specificactivities). Additionally, training data may include sensor data from adevice, such as device 112, 126, 128, 130, and/or 400. In this way, thetraining day that may store information related to one or more sensoroutputs in addition to information related to one or more volumes ofoxygen consumed during one or more activities. In one implementation thesensor data stored in addition to the oxygen consumption data mayinclude data from one or more of an accelerometer, a gyroscope, alocation-determining device (e.g., GPS), light sensor, temperaturesensor (including ambient temperature and/or body temperature), heartrate monitor, image-capturing sensor, moisture sensor and/orcombinations thereof. For example, training data may include stored datafrom an accelerometer sensor, in addition to information related to avolume of oxygen consumed during an activity, for example, playingsoccer.

Accordingly, the systems and methods described herein may includetraining data that includes one or more calculated attributes associatedwith an activity. Furthermore, the one or more attributes associatedwith an activity may include one or more volumes of oxygen consumed perunit mass of a person at one or more time points during an activity,and/or one or more calculated values based on one or more outputs fromsensors associated with a device monitoring one or more motions of auser during said activity. For example, in one implementation, an outputof an accelerometer sensor may include an acceleration value for each ofthe three axes (x-, y-, and z-axis). Accordingly, in one implementation,a plurality of acceleration values associated with the respective axesto which an accelerometer sensor is sensitive (x-, y-, and z-axis) maybe grouped as a single acceleration data point. In anotherimplementation, acceleration values may be processed by a processor,such as processor 202, associated with a device, such as device 112,126, 128, 130, and/or 400 to calculate one or more attributes.

In one example, one or more data points received from a sensoraggregated into a dataset representative of one or more motions of user.Accordingly, the one or more data points may be processed to representthe data in a way that allows for one or more trends and/or metrics tobe extracted from the data. In one example, acceleration data outputfrom an accelerometer may be processed (transformed) to calculate avector normal (“vector norm”). Additional or alternative transformationsmay be employed to calculate, in one example, a standard deviation ofthe vector normal, a derivative of the vector normal, and/or a FastFourier Transform (FFT) of the vector normal. Those skilled in the artwill appreciate that certain transformations may combine sensor datafrom two or more sensors and/or with other data, such as biographicaldata (e.g., height, age, weight, etc.). In other embodiments,transformation may only utilize data from single sensors, such thatsensor data from multiple sensors is not mixed.

In one example, one or more attributes may be calculated from thereceived data, and wherein the attributes may be calculated subsequentto one or more transformation processes be executed on a datasetrepresentative of one or more motions of a user. In this regard,datasets from multiple users may be used as a comparison against a userwhose activity is not within the dataset. This may be done withoutcategorizing the user's activity into an activity type (e.g., walking,running, playing specific sport) and in certain embodiments, the usermay be performing an activity that was not conducted as part ofcollecting the training data within the data used to obtain attributevalues for the models.

In another example, the systems and methods described herein may beimplemented to estimate one or more metrics from sensor data. Thesemetrics may include, among others, an estimation of energy expenditure,an estimation as to whether a user is running, walking, or performingother activity, and/or an estimation of a speed and a distance (a pace)which a user is moving, and the like. For example, block 601 offlowchart 600 from FIG. 6 shows an example implementation of calculatingenergy expenditure from one or more attributes. Separately, block 603 isdirected to an example implementation for calculating speed, which maybe determined using one or more calculated attributes, which may bederived from the same sensors, from the same data, and/or the sameattributes as the energy expenditure metric.

Accordingly, the systems and methods described herein may utilize datareceived from one or more different sensor types, including, amongothers, an accelerometer, a heart rate sensor, a gyroscope, alocation-determining device (e.g., GPS), light (including non-visiblelight) sensor, temperature sensor (including ambient temperature and/orbody temperature), sleep pattern sensors, image-capturing sensor,moisture sensor, force sensor, compass, angular rate sensor, and/orcombinations thereof. Furthermore, one or more components of flowchart600 from FIG. 6 may be implemented by a first device that utilizes afirst operating protocol, and using data received from a second devicethat utilizes a second operating protocol, and the like.

Furthermore, while the example of attributes associated withacceleration data output from an accelerometer sensor are described,those of ordinary skill will appreciate that other sensors may be used,alone or in combination with other sensors and devices, withoutdeparting from the scope of this disclosure. For example, a heart ratemonitor may be used, wherein the data output from a heart rate monitormay output data representative of a heart rate in units of beats perminute (BPM) or equivalent. Accordingly, one or more transformations maybe performed on outputted heart rate data to interpolate a heart ratesignal between heart rate data points, and allowing for signal dropoutsat certain points. Furthermore, the attributes calculated for sensordata associated with a heart rate monitor, or any other sensor, may bethe same, or may be different to those described above in relation toaccelerometer data.

In another implementation, the systems and methods described herein mayanalyze sensor data from combinations of sensors. For example, a devicemay receive information related to a heart rate of a user from a heartrate monitor, in addition to information related to motion of one ormore appendages of a user (from one or more accelerometers, and thelike). In one example, the device may determine that a user has a heartrate indicative of vigorous exercise, but accelerometer data mayindicate that said user has been at rest for a period of time.Accordingly the device may determine that the user has a sustainedelevated heart rate after a period of activity but is now resting aftersaid activity, and the like.

In one implementation, training data may be used to construct one ormore models, otherwise referred to as experts, or expert models, forpredicting, among others, a volume of oxygen consumption based upon (atleast in part) one or more individual-specific properties such as agender, a mass and/or a height of a user. Accordingly, information fromone or more sensors associated with a device, such as device 112, 126,128, 130, and/or 400, may be used to calculate one or more attributes.In turn, the calculated attributes may be compared to attributesassociated with one or more constructed models, and thereby, used topredict a volume of oxygen being consumed by a user while outputtingmotion signals (sensor output values) corresponding to the calculatedattributes. For example, a user may be performing an activity, such asplaying soccer, while wearing a sensor device on an appendage. Thesensor device, in turn, may output sensor values, which may be processedto calculate one or more attributes. Subsequently, the one or morecalculated attributes may be compared to one or more attributesassociated with one or more models, and an estimation of a volume ofoxygen being consumed by the user while playing soccer may be made.Furthermore, said estimation of a volume of oxygen being consumed may beused to estimate energy expenditure values by the user playing soccer.This process is described in further detail in relation to FIG. 6. Incertain embodiments, all of the sensor data comes from a unitary device.In one example, the unitary device is an appendage-worn device. Incertain configurations, the appendage worn device comprises at least oneof an accelerometer, a location-determining sensor (e.g., GPS) and aheart rate monitor. In another example, the unitary device comprises asensor configured to be placed on or within athletic apparel, such as ashoe. In yet another example, a sensor from at least two differentdevices are utilized to collect the data. In at least one embodiment,the device comprising a sensor utilized to capture data is alsoconfigured to provide an output of energy expenditure. In oneembodiment, the device comprises a display device configured to displayan output relating to energy expenditure. In further embodiments, thedevice may comprise a communication element configured to transmitinformation relating to energy expenditure to a remote device.

In another implementation, one or more attributes may be calculated fromreceived sensor data and used as inputs to one or more walking and/orrunning models for predicting, among others, a speed/a pace of a user.Further details of such an implementation are described in relation toblock 603 of flowchart 600.

FIG. 6 is a flowchart showing an exemplary implementation of attributecalculation. In one example, this attribute calculation may be used toestimate one or more metrics associated with an activity being performedby a user, and wherein said estimation may include an energy expenditurespeed, and/or one or more other metrics. In one example, an energyexpenditure and/or speed of walking and/or running may be estimatedusing a same set of attributes, or a sub-set of attributes from a commongroup of attributes, and the like.

Information related to the movement of the user may be outputted as oneor more data signals from one or more sensors associated with one ormore sensor devices monitoring the user. In one implementation, FIG. 6represents one or more processes carried out by at least one processor,such as processor unit 202, which may be associated with a sensordevice, such as, among others, device 112, 126, 128, 130, and/or 400.Accordingly, devices may not directly monitor a volume of oxygen beingconsumed by a user during an activity. In one implementation, one ormore sensor devices may monitor one or more motions associated with oneor more activities being performed by a user. Furthermore, in onearrangement, received activity data may be correlated with observedoxygen consumption values for activities that may exhibit certainattributes, and associated with one or more oxygen consumption models.

One or more embodiments receive sensor data from one or more sensors(see, e.g., block 602). In certain embodiments, the sensor data may beassociated with a device worn by a user. In one example, and aspreviously described, said device may be, among others, device 112, 126,128, 130, and/or 400. Accordingly, the sensor data may be received by aprocessor, such as processor 202 from FIG. 2, and may be received fromone or more sensors described herein and/or known in the art. In oneimplementation, sensor data may be received at block 602 from anaccelerometer at a frequency of, among others, 25 Hz. Additionally oralternatively, sensor data may be received from the sensor, such asaccelerometer, in windows of between 5.0 and 5.5 seconds. In oneembodiment, the window (or time frame) may be about 5.12 seconds inlength. A window may be a period of time during which sensor data isrecorded for one or more motions of a user associated with one or moreactivities being performed by a user. In one implementation, a samplewindow may include 128 samples (data points) of sensor data, wherein asample of sensor data may include a value for each of three orthogonalaxes of an accelerometer (x-axis, y-axis, and z-axis), and/or a vectornormal value. In yet another implementation, sensor data received from,in one implementation, an accelerometer, may be received in windows thatdo not overlap (e.g., 5.12 second-length groups of sensor data eachcontaining 128 samples, and received singly, rather than simultaneously,and/or discrete from each other). However, in alternative embodiments,those of ordinary skill will readily understand that the systems andmethods described herein may be employed with any frequency of operationof an accelerometer, with a window length measuring any length of time,and using any number of samples of sensor data from within a givenwindow. Data may be validated as it is received, such as for, forexample, at block 604. Data validation may include, among others, aperson of one or more values of received sensor data to one or morethreshold values, and the like.

Further aspects of this disclosure relate to calculating one or moreattributes from the data (see, e.g., block 606). Calculation of one ormore attributes may occur after validation protocols, such as thosedescribed herein. In one implementation, one or more attributes may becalculated for one or more of the received samples in a sample window(e.g., the 128 sample window described above). Attribute calculation mayoccur in real-time as the data is collected

Further embodiments may compare one or more calculated attributesassociated with data received from one or more sensors, and indicativeof one or more activities being performed by a user, to one or moreattributes associated with one or more models. In one example, one ormore attributes may be compared to one or more models (see e.g., block608). For example, for calculations of energy expenditure, one or moreattributes may be compared to oxygen consumption models. In anotherexample, attributes may be used as inputs to one or more of, amongothers, models for estimation of a number of steps (during walking),strides (during running), or other movements by a user, and/or modelsfor estimation of speed and distance (pace) of a user (see e.g., block603 of flowchart 600). Furthermore, as will be apparent from FIG. 6, oneor more calculated attributes may be used as inputs to one or moremodels such that, in one example, a model for estimation of an energyexpenditure may be executed separately from a model for calculation of astep rate, a walking speed, and/or a running speed, and the like. Aspreviously described, one or more models may be stored in a memory, suchas memory 212, and the like, and associated with a device, including asensor device.

In one implementation, a model may include information (e.g. trainingdata) collected during one or more user's performance conducting one ormore activities and, in one example, predicted oxygen consumption. Themodels may include training data from activities that, despite beingdifferent from the activity that an athlete is performing, may havesimilar relationships between attributes. As such, the models may serveas accurate predictors of oxygen consumption. Accordingly, a model mayinclude training data associated with one or more different activities.For example, a model may include training data received from one ormonitoring processes associated with, among others, playing soccer andplaying basketball. In this way, oxygen consumption data associated withcertain movements of soccer and basketball activity data may be similar(within one or more predetermined numerical ranges for different periodsduring the activities, and the like).

In another implementation, a first oxygen consumption model may comprisedata from a same one or more users as those one or more users' data usedin a second oxygen consumption model. In another configuration, a firstmodel and a second model may use a same one or more users' data. In yetanother configuration, a data associated with a model may have beencaptured from a same one or more users during a single data collectionperiod, or from multiple collection periods across a same or a differentday, and the like. In one implementation, a first model may beassociated with data from a first group of one or more sensors and asecond model may be associated with a second group of one or moresensors, and wherein the first group may share one or more, or none ofthe same sensor types. In one implementation, the systems and methodsdescribed herein may compare calculated attributes from activity data(real-time activity data, and the like) to one or more models whereinthe one or more models may not include data captured for that activitytype. In this way the one or more models may be agnostic to the specificactivity being performed by a user. For example, an activity device mayreceive information from a user performing a basketball activity. Inresponse, the device may process the received basketball activity data(such as, for example, block 606 of flowchart 600), and comparecalculated attributes to one or more models (such as, for example, block608). In one implementation, the one or more models may or may notcomprise data related to basketball. In this way, the computed one ormore attributes for received sensor data may correspond to one or moremodels, and wherein the models need not comprise training data relatedto the specific activity being performed by a user.

In one configuration, a plurality of models, each with their ownattributes, which may or may not overlap attributes of other models, maybe utilized. In one example implementation, each model may be associatedwith 20 attributes. In another configuration, the systems and methodsdescribed herein may store 5, 10, 15 or 21 attributes for each model.However it will be readily understood to those of ordinary skill thatthe systems and methods described herein may store any number ofattributes in association with each model, and in certain embodiments afirst number of attributes stored in association with a first model maydiffer from a second number of attributes stored in association with asecond model. Furthermore, the one or more attributes stored inassociation with a model may be alternatively referred to as weights, ormay be used to calculate related weighting values that may be used tocompare a model to those attributes calculated from sensor data receivedfrom a device. Accordingly, in one implementation, the number ofattributes calculated from data received from a sensor device (ormultiple sensor devices) collecting movement data of a user may be equalto a number of attributes, or alternatively, a number of weightsassociated with one or more stored oxygen consumption models.

One or more aspects may calculate a probability that a first model willprovide the most accurate estimation of oxygen consumption from thetotal number of stored one or more models. For example, a probabilitythat a specific model, from a group of different (e.g., 16) models ismost likely to provide the most accurate output of oxygen consumptionmay be calculated. The calculations may be performed, for example, aspart of block 608, and be based on one or more attributes calculatedfrom received sensor data indicative of an activity being performed by auser. In one implementation, a level of closeness between attributescalculated from received sensor data, and those attributes, oralternatively, those weights, associated with one or more of the storedoxygen consumption models may be calculated, such as for example as partof block 608. In one implementation, the systems and methods describedherein may execute one or more processes to compare input attributeswith corresponding weights associated with a stored model (expert). Inone example, block 608 may calculate a probability for each storedmodel. Accordingly, said probability may represent a likelihood that thecalculated one or more attributes are a best-fit for a given model. Forexample, a probability may be calculated for each of 16 stored models,and the like. The highest probability value, from the 16 calculatedprobabilities, indicates the model with the best-fit for the calculatedattributes, and the like.

In one example, block 610 represents one or more processes to select amodel with a best-match, or best-fit to those calculated attributes fromblock 608. As previously described, said best-fit model representsstored training data that most closely approximates the data receivedfrom a sensor device monitoring an activity of the user. In oneimplementation, the best-fit model may be the model corresponding to acalculated probability value that is closest to a value of 1.0. Inanother implementation, block 610 may select two or more models. Forexample, models fitting within a predefined deviation, variation, and/orthreshold may be selected (referred to as a hybrid-model strategy).

As shown by illustrative block 612, one or more embodiments may estimatean output value from a selected model (expert). In one example, anoutput may be in estimation of a volume of oxygen consumption as aresult of one or more activities being performed by a user. Accordingly,in one example, block 612 may correlate an activity being performed by auser with an estimated oxygen consumption value, and based on a selectedmodel (e.g., a best-fit model) that most closely matches attributevalues calculated from sensor data. In one implementation, a model maystore one or more estimates of oxygen consumption, wherein the one ormore estimates of oxygen consumption may be stored based on at least oneindividual-specific value, such as a gender, a weight and/or a height ofa user. Accordingly, and based upon the one or more attributesassociated with the sensor data received from a sensor device, block 612may return an estimate of a volume of oxygen consumption by the userbased upon an activity carried out by the user.

Block 614 may be implemented to estimate a motion metric associated witha model output value. In one example, a motion metric may be an energyexpenditure value, which may be estimated using an oxygen consumptionvalue. As previously described, one or more methods may be utilized tocalculate an energy expenditure from an oxygen consumption value. In oneexample, an estimated energy expenditure of 5 kcal is associated with anoxygen consumption of 1 L by a user. However those of ordinary skill inthe art will recognize various other formulae for calculating energyexpenditure based upon a value of oxygen consumption, and using one ormore individual-specific values (e.g., a height and a weight of a user).

In another implementation, one or more attributes calculated (including,e.g., at block 606) may be used in a determination of whether sensordata is indicative of walking, running, or another activity beingperformed by a user, and additionally or alternatively, the speed atwhich the user is walking or running, and the like. Accordingly, one ormore attributes calculated at block 606 may be used as inputs to block603. In particular, one or more attributes may be communicated fromblock 606 to decision block 616. At block 616, one or more processes maybe executed to determine whether a user is running or walking, orperforming another activity. If it is determined that a user isperforming an activity other than running or walking, flowchart proceedsfrom block 616 to 618. Accordingly, for a user performing activity otherthan running or walking, no processes are executed to define the speedat which the user is traveling, and the like. If it is determined, atdecision 616, that's the user is running or walking, decision 620 may beimplemented to determine whether the activity is walking or running.Example embodiments for selecting an activity, such as running orwalking, are provided herein, including in relation to In oneembodiment, if it is determined that a user is running, one or moreattributes may be communicated to a running model, such as to determinespeed (e.g., see block 622). If, however, it is determined that a useris walking, certain embodiments may communicate one or more attributesto a walking model, such as to determined speed (e.g., block 624).

In another implementation, the systems and methods described herein maybe utilized to calculate energy expenditure using one or more metricsreceived from an activity device, such as device 112, 126, 128, 130,and/or 400. As such, the one or more metrics may include activitymetrics related to an activity being performed by a user. Examples ofactivity metrics may include, but are not limited to, speed, pace,acceleration, volume of oxygen consumption, a metabolic equivalentvalue, a calorie consumption value, a power, a heart rate, a gradient,or a temperature, among others. Accordingly, in one example, flowchart700, schematically depicted in FIG. 7A, may describe one or moreprocesses for calculation of an energy expenditure value of a user fromdata received from an external device. Further, in one implementation,one or more components of flowchart 700 may be executed by one or moredevices utilizing a first operating protocol, and using data receivedfrom one or more separate devices utilizing a second operating protocol.As such, these first and second operating protocols may haveincompatibility issues, as previously described in this disclosure.

In one specific example, one or more components of flowchart 700 andFIG. 7A may be executed by a device, such as device 112, 126, 128, 130,and/or 400, as previously described. In one embodiment, sensors solelylocated on a device configured to be worn by a user, such as awrist-worn device, may be utilized to detected motion parameters. Datafrom sensors on such as device may be used without the assistance ofother sensors in one or more determinations relating to classifyingactivity and/or determine energy expenditure. The activity may includeathletic and/or other physical activity of user 124. In oneimplementation, FIG. 7A schematically depicts flowchart 700 having anillustrative process that may be utilized to classify activity and/orcalculate energy expenditure values of an individual in accordance withone embodiment. FIG. 7A is provided as an overview of exemplaryembodiments that may comprise a plurality of sub-elements. In thisregard, the remaining figures (and related disclosure) following FIG. 7Amay optionally be used in conjunction with FIG. 7A and/or each other toprovide a full system that obtains sensor data and provides energyexpenditure values. In accordance with other embodiments, one or moredifferent systems and methods discussed below may be used alone or incombination with only a portion of other disclosed systems and methodsto provide one or more of: step counts, activity classifications, andenergy expenditures, among others. Various embodiments of stepquantification systems and methods may relate to a low power, highfidelity, integer-based step counter using a multi-tier technique. Theseand other embodiments are described below.

In accordance with a first embodiment, a plurality of samples from oneor more sensors associated with one or more devices (e.g., devices 112,126, 128, 130, and/or 400) may be obtained during a first time period(see, e.g., block 702). In certain configurations, at least one sensor(e.g. sensor 128) may comprise an accelerometer. The accelerometer maybe a multi-axis accelerometer. In another embodiment, however, aplurality of accelerometers may be utilized. Other non-accelerometerbased sensors are also within the scope of this disclosure, either incombination with an accelerometer or individually. Indeed, any sensor(s)configurable to detect or measure athletic movement and/or physiologicproperties are within the scope of this disclosure. In this regard, datamay be obtained and/or derived from a plurality of sensors, includingfor example, location sensors (e.g., GPS), heart rate sensors, forcesensors, gyroscope, etc. In one embodiment, various systems and methodsare implemented, at least partially, on a portable device. In certainembodiments, the portable device may be a wrist-worn device (see, e.g.,sensor 128). In one embodiment, sensor data from a device configured tobe worn on a human appendage (e.g., wrist, arm, neck, ankles, leg, etc.)may be utilized without other sensor data. Motion data, such as measuredthrough an accelerometer and/or other sensors, may be loaded into amulti-segment threshold based acceleration buffer.

Further aspects relate to detecting and/or measuring an athleticparameter, such as for example, a quantity of steps taken by a user,such as user 124. One or more system or methods may utilize variousportions of the data (such as in an acceleration buffer comprisingaccelerometer data) to determine if detected parameters are indicativeof a specific action or activity. In one embodiment, a quantity of stepsmay be detected during a predefined period of time (See, e.g., block704). Various different systems and methods may be utilized to quantifya number of steps taken by the user during a time period (or evendetermine whether steps exist in the sensor data). In oneimplementation, systems and methods for quantifying steps taken by userover a given time period are described in FIGS. 4-8 of U.S. applicationSer. No. 13/744,103, filed 17 Jan. 2013, the entire contents of whichare incorporated herein by reference for any and all non-limitingpurposes. In one example, step data and/or other motion data may beutilized in the classification of activity, such as either walking orrunning, for example (see, e.g., block 706). In certain implementations,if data cannot be categorized as being within a first category (e.g.,walking) or group of categories (e.g., walking and running), a firstmethod may analyze collected data. In one example, if detectedparameters cannot be classified, then a Euclidean norm equation may beutilized for further analysis. In one example, an average magnitudevector norm (square root of the sum of the squares) of obtained valuesmay be utilized. In yet another example, a different method may analyzeat least a portion of the data following classification within a firstcategory or groups of categories. In one example, a step algorithm, suchas those disclosed herein, may be utilized.

Further examples may utilize the classified activity data and/orunclassified activity data to estimate the energy expenditure of theuser's detected motions as sensed by one or more of the sensors (e.g.,block 708).

Further examples relate to adjusting energy expenditure values accordingto at least one activity factor. In some examples there is not aone-to-one correlation between an activity and an activity factor. Theselection of an activity factor may be based on several differentvariables, such as the activity identified, steps taken, heart rate, andintensity of a workout.

Aspects of various examples may offer one or more advantages and/orbenefits over the prior-known systems and methods. In certainimplementations, false positives are reduced or eliminated forshort-duration arm movements using a buffer filling strategy. Using aconstrained search for analysis (e.g. FFT) may assist in selecting thecorrect frequency (e.g., frequencies relating a vertical bounce ratherthan the arm swing such that the correct walking frequency is obtainedfor two feet steps). In further examples, the overlapping of motion datawindows may allow for improved detection of short bursts of activities(e.g., step activities). Finally, the frequency analysis may beperformed on one combined channel of sensors so that arm rotation doesnot throw off detection and measurement of sensor outputs. Furthermore,by combining accelerometer channels, less analysis (e.g. Fouriertransform frequency analyses) may be performed. This may improve batterylife. One or more of these advantages may be realized on a portabledevice configured to be worn on an appendage of the user during theperformance of the physical motions.

In one implementation, process 700 may normalize a calculated energyexpenditure value. As such, in one example, process 700 may execute oneor more normalization processes at block 710.

Accordingly, one or more normalizing/normalization processes, such asthose executed at block 710, may be used to calculate an athleticmetric, such as a cumulative energy expenditure value, which may beexpressed as, for example, caloric expenditure. Based on one or morecriterion, such as the classification of the user activity, one or morenormalizing factors (or processes utilizing one or more factors) may bedetermined. For example, in one embodiment, at least one of user's 124activity may be classified (e.g., such as at block 710 of flowchart 700shown in FIG. 7A, or block 734 of flowchart 720 shown in FIG. 7B) andone or more corresponding normalizing factors applied to the athleticmetric. In one example, application of normalizing factors to activityclassification (or other variable(s)) to create a normalized athleticmetric may allow for an improved comparison of activity levels ofathletes and/or may promote collaboration among users, normalize resultsfor competition among users of different capabilities, and otherwiseencourage activity. In one embodiment, a normalized energy expenditurevalue may be calculated as follows:

NEEVs=AF*duration  (equation 1)

Wherein:

NEVs=Normalized Energy Expenditure Values

AF=activity factor

duration=duration of the activity classified in step 706

Block 706 may be performed at a device that includes sensors thatmonitor activity and/or at another device that includes a processor,such as a mobile phone (see, e.g., 112) or server (see, e.g., 111).

In some embodiments equation 1 may be modified to include a scalar thatis multiplied by the activity factor and duration. The scalar may beselected so that typical energy normalized expenditure points fallwithin a desired range. The range of values may be desired for variousgames or competitions.

Variations of equation 1 may be used in other embodiments of theinvention. For example, NEEVs (or other metrics) may be calculated withformulas that utilize a MET value, a RMR value, a duration and/or ascalar. In one embodiment, NEEVs calculated as follows:

NEEVs=MET value*duration*RMR*scalar  (equation 2)

Wherein:

MET value=metabolic equivalence value, which may be determined in block704

duration=duration of an activity, such as an activity classified inblock 706

RMR=resting metabolic rate

Scalar=a number, which may be uniformly applied or variable based uponone or more factors.

The RMR used may be a calculated RMR or a default RMR. A calculated RMRmay be a function of factors such as a user's height, age, weight,gender and/or other demographic or physiological parameters. In certainembodiments, one or more factors may be obtained and/or derived from oneor more sensors associated with one or more of devices 112, 126, 128,130, and/or 400, and thus may not have to be specifically defined byuser 124 or source of data associated with the received athletic metric.A default RMR may be a value believed to correspond a relatively largenumber of users. In alternative embodiments a default RMR may correspondto an RMR of a well-known athlete, celebrity or a famous person. The useof a default RMR may allow the comparison of activity levels among manydifferent users that otherwise could not or would not be compared. Adefault RMR may also promote collaboration among users, normalize forcompetition among users of different capabilities, and/or otherwiseencourage activity.

As shown in equation 2, a scalar may be optionally implemented. Thescalar used in equation 2 may, but is not required to be, a wholenumber, such as 1, 3, 5 or 10. In some embodiments, the scalar isselected so that typical energy expenditure points fall within a desiredrange. The range of points may be desired for various games orcompetitions, and/or to provide a relation to a virtual currency orpoint system. Different and varying equations may be selected fordifferent games, activities, population dynamics, and competitions. Inone example a group may set handicaps among the players based onfitness, so that NEEVs may only be generated for those athletes that doa common activity or set of activities for longer period(s) of time. Agroup of users participating in an energy expenditure point competitionmay agree on a particular equation or method before beginning thecompetition. Yet, in other embodiments, NEEV calculation may not beexplicitly disclosed to one or more athletes or users so as to preventor attempt to prevent athletes from attempting to maximize points orotherwise “earn” values from conducting activities that exploit one ormore imprecisions in the system. As those skilled in the art willappreciate, every model will have limitations. And in this regard,aspects of this innovation attempt to minimize inaccurate and/orimprecise data values via one or more systems and methods disclosedherein. In some embodiments of the invention, a user may participate inmultiple competitions and earn different values for the same activitybecause of different calculation methods. For example, a user may beparticipating in two competitions that have unique calculation methods.The user may earn two different point totals for the two different gamesand a third point total for their overall energy expenditure. Some pointtotals may be maintained separate from an overall point total.

In those embodiments in which the athletic metric relates to energyexpenditure in which METs are utilized, some activities, games and/orcompetitions may limit awarding energy expenditure points for activitiesthat have relatively low MET values. In some embodiments, awardingenergy expenditure points for activities that have relatively low METvalues may also be limited all of the time or in other situations. Anoptional step may be added to the process shown in FIG. 7A or otherimplementations to not award points for activities that do not exceed aMET threshold value. For example, an exemplary threshold value may be1.0, 2.0 or 3.0. In another embodiment, the threshold value may equal2.8. Different games and competitions may use other threshold values.When the MET value does not exceed the threshold, a step may beimplemented to disregard the corresponding activity and not use theactivity when calculating energy expenditure points.

Another embodiment could have the threshold generally applied, but notwhen games or competitions are underway, or at least certain games orcompetitions. The games or competitions may be based on all values. Inanother embodiment, a threshold may always apply even to games andcompetitions. In another embodiment, different thresholds may apply byactivity, game and/or competition, e.g., one for running briskly, onefor running, one for walking, and a default.

In certain implementations, such as after at least one of user's 124activity is classified according to one embodiment, block 704 may beimplemented to determine a corresponding MET value. For example, the METvalue may correspond to brisk running, running at a moderate pace,walking slowly or any other activity found in conventional MET tables.If an activity was not classified, such as in block 704, a default METvalue may be selected or derived. In some embodiment multiple defaultMET values may be utilized. An activity's intensity, duration or othercharacteristic(s) may be assessed, from which one or more default METsmay be applied. These plural METs may be set via medians/averages,ranges, or other statistical approaches.

Alternative embodiments may use alternative or additional equations forcalculating point values and/or other quantities. The equations mayinclude derivations of measured and/or calculated values. Derivationsthat include time periods may be used to show rates and rates of change.For example, one equation may be used to determine a rate ofaccumulating activity points or energy expenditure points. Anotherequation may be used to determine a quantity of activity points orenergy expenditure points accumulated over a predetermined time period.

Some equations may use variables other than time. For example, someequations may be used to calculate a value as a function of activityvalues or normalized energy expenditure values and/or steps. Calculatingvalues that are functions of activity values or NEEVs and othervariables may be used to compare the efficiencies of various activities.For example, an equation may be used to determine that taking steps at afaster pace may result in activity values or NEEVS accumulating at afaster per step pace. Another exemplary equation may determine activityvalues or NEEVs per a predetermined distance or a unit of distance.

Some equations may be used to calculate first and/or second derivativesof measured or calculated values to show rates and rates of change. Forexample, an equation may be used to calculate or estimate a rate ofaccumulation of activity points or energy expenditure points at a giventime. In some embodiments an instantaneous rate of accumulation ofactivity values or NEEVs may be displayed to a user via display 235 or adisplay that is part of a mobile device.

Either before, during and/or after the normalized values are calculated,the calculated values (raw, processed, or NEEVs) may be combined, suchas being added, to a total in step 708. The total may allow user 124(and/or selected individuals or groups approved by user 124) to see howmany points are earned over various periods of time, such as days, weeksand months. Totals may also be calculated for multiple time periods. Forexample, a user may receive totals for periods that include 24 hours,one week, one month and one year. In some embodiments users may selectother time periods or deselect time periods. A user may track multipletime periods concurrently and track points award since the beginning ofuse of a device or start of a program. The total for any giving timeperiod may represent points earned for several activities. For example,in a day a user may receive points for walking, jogging and sprintingduring different time periods. As mentioned above, the values earned foreach activity may be a function of a corresponding activity factor.

Various additional or alternative methodologies for calculation ofenergy expenditure of a user from received activity metrics may beutilized, without departing from the scope of the disclosures describedherein. FIG. 7B schematically depicts one implementation of flowchart720 that may be executed to calculate energy expenditure of a user.Accordingly, in one example, flowchart 720, similar to flowchart 700,may be executed by a first device, such as device 112, 126, 128, 130,and/or 400, and utilizing data received from a second device, such asdevice 112, 126, 128, 130, and/or 400. Further, the first device and thesecond device may be associated with different operating protocols, ormanufacturers, among others.

In one example, flowchart 720 may receive data from one or more sensordevices. Those of ordinary skill in the art will recognize that theseone or more sensor devices may be worn by a user, or may generate sensordata using remote sensing of user movement (e.g. remote imagingtechnologies configured to track user movement, and the like). As such,flowchart 720 may receive sensor data communicating information relatedto an activity being performed by a user. The sensor may comprise one ormore of an accelerometer, a GPS sensor, a heart rate sensor, abarometric pressure sensor, a thermometer, an ambient light sensor, or acompass sensor, among others. In one example, the sensor may be includedin one or more of devices 112, 126, 128, 130, and/or 400, as previouslydescribed in this disclosure. Accordingly, in one implementation, sensordata may be received at block 722 of flowchart 720.

In one implementation, received sensor data may include informationidentifying an activity being performed by a user. As such, receivedsensor data may include information identifying a sport, or anotherphysical activity, that a user is currently participating in, orpreviously participated in. In one example, one or more activityrecognition processes may be executed by a sensor device in order toidentify one or more activities being participated in by a user. Thoseof ordinary skill in the art will recognize various activity recognitionprocesses that may be executed in this regard. As such, in one example,an activity may be identified from received sensor data at block 724. Inthis way, in one implementation, one or more activity recognitionprocesses may be executed separately to flowchart 720 by a separatedevice. However, those of ordinary skill in the art will recognize thatone or more activity recognition processes may be executed at block 724to identify an activity associated with received sensor data, withoutsaid activity being explicitly identified in received sensor data. Assuch, in one example, a receiving device may identify one or more sensordata patterns from received sensor data in order to identify one or moreactivities being participated in by a user.

In one example, one or more activity metrics may be identified fromreceived sensor data. These activity metrics may include values utilizedto quantify one or more physical movements of a user. As such, anactivity metric may include, among others, a speed, a pace, an inclineon which a user is moving, a heart rate, a metabolic equivalent value, acalorie consumption value, a volume of oxygen consumption, or a powervalue. In this way, flowchart 720 may receive one or more activitymetrics calculated by one or more separate devices, such as one or moreof devices 112, 126, 128, 130, and/or 400. In one example, flowchart 720may identify these one or more activity metrics at block 726.

In one implementation, in order to calculate an energy expenditure for auser, one or more personal activity metrics may be retrieved from adatabase. For example, an energy expenditure of a user may be based uponone or more of a user height, weight, a gender, a resting heart ratevalue, and/or a metric recording a baseline level of physical fitness,among others. As such, these one or more personal activity metrics maybe stored in a database, such as in one or more of devices 112, 126,128, 130, and/or 400, or server 111. Accordingly, those of ordinaryskill in the art will recognize various network communication topologiesand/or protocols that may be utilized to access database informationstored remotely or locally, or combination of both, and withoutdeparting from the scope of the disclosures described herein. In oneexample, flowchart 720 may retrieve one or more personal activitymetrics from a database at block 728.

In one implementation, in order to calculate an energy expenditure valueof the user, flowchart 720 may utilize one or more activity-specificformulae. For example, an energy expenditure value may be calculatedbased upon an activity identified at block 724, and using one or moreformulae specific to the identified activity. As such, in one example,flowchart 720 may retrieve activity-specific formulae from a database,such as from one or more of server 111 and/or devices 112, 126, 128,130, and/or 400. In one example, the activity-specific formulae may beretrieved from a same database as the personal activity metrics fromblock 728. In one implementation, the retrieval of theseactivity-specific formulae may be executed at block 730.

In one example, flowchart 720 may execute one or more processes tocalculate an energy expenditure of the user using the received sensordata in combination with the one or more retrieved personal activitymetrics and activity-specific formulae. As such, in one example, acalculated energy expenditure for a user may output a numerical valuecorresponding to energy used by a user over a given time period whileparticipating in one or more physical activities. In one example, acalculated energy expenditure may be expressed in units of joules orcalories, among others. Accordingly, the calculation of energyexpenditure value for user may be executed at block 732 of flowchart720.

In one implementation, one or more processes may be executed tonormalize a calculated energy expenditure value for a user. As such,this normalization may allow for comparison of a calculated energyexpenditure value to corresponding energy expenditure values calculatedfor one or more other individuals, and such that factors of gender,height, weight, and/or a baseline level of physical fitness are takeninto account. Accordingly, this normalization may be executed at block734 of flowchart 720, and may be similar to one or more processesexecuted at block 710 of flowchart 700.

FIG. 8A illustrates an example flow diagram 800 which may be used incalculating a combined energy expenditure estimate for a user 124 basedon energy expenditure estimates obtained by two or more differentdevices, which may entirely different devices incapable of communicatingwith each other, either directly or indirectly. In accordance with oneexample embodiment, a 1st athletic metric value may be derived from afirst process which may be conducted on a 1st device from 1st source(e.g., block 802). For example, the first process may calculate energyexpenditure information associated with a first athletic activityperformed by the user 124. The first source may be an electronic storageof data (e.g., non-transitory computer-readable medium) configured todetermine values or obtain data from which values may be calculated fora user base. In some cases, the first energy expenditure information mayinclude a first energy expenditure estimate and one or more differenttime stamps, such as first time stamp associated with a start of a firstathletic activity and/or a second time stamp associated with an end ofthe first athletic activity. The process may calculate the value for themetric based upon a specific process, such as at least a portion of thatdisclosed by flowchart 600. In yet further embodiments, other metric(s)may be calculated, such as pace, acceleration, either alone and/or aspart of, or independently of energy expenditure. (FIG. 13 showsflowchart 1300 providing an example utilization of two or more differentmetrics). Thus, examples with reference to energy expenditure are merelyexamples and not intended to limit the scope of this disclosure. Ininstances where data regarding certain parameters, such as energyexpenditure, are obtained from two or more sensors or determined bydifferent mathematical calculations, certain aspects of this disclosuremay be implemented to determine a resultant output. In such cases, thecombined values may be calculated using one or more of an average, aweighted average or a statistical solution. However, such calculationsmay not be practical in every situation. As such, another method ofcombining information gathered using different devices may be desired.

A 2nd athletic metric value that is derived from 2nd process conductedon a 2nd device may be received from a 2nd source. For example, insteadof determining energy expenditure using data from the same firstcollection of sensors used in the first process, a second collection ofsensors may have been utilized, and/or a different process may be usedto determine the value. For example, a first process may use a data fromthe same sensors (or portion thereof) to classify the user's athleticmovements into an activity type or category, whereas a second processmay not classify the movements into a category or type. In oneembodiment, the first source may be a database of a user's datacollected from their GPS device, whereas the second source may be asecond database that collects user data from a tri-axis accelerometer.The first source may have an online user interface that allows the userto log in using first credentials and the second source may have anonline user interface that allows the same user to log in using secondcredentials. Because the first and second source may be competitorservices, they may be independent of each other, thus the devices usedto collect the data and/or provide the data to the non-transitorydatabase, and/or calculate the metric, may not be configurable tocommunicate, either directly or indirectly. Thus, the devices may not beable to be directly updated with information from the other source(s).

In certain embodiments, a modification scalar may be applied to at leastone of the athletic metric values (e.g., block 806). As discussed above,parameters relating to the user's activity may be captured usingsensors, which may be located on one or more devices. The sensors maymonitor the user's motion, physiological properties, and/or otherparameters. A first sensor may result in more accurate determinations,such as for example, energy expenditure calculations while a user isrunning and a second sensor may result in more accurate determinations,e.g., energy expenditure calculations, while a user is performingsquats. For example, a multi-axis accelerometer and/or force sensor maybe more accurate for skateboarding or basketball than GPS would be, andon the flip side, a GPS sensor may be more accurate when measuring ahike than a tri-axis accelerometer. However, a GPS sensor combined witha force sensor still may provide even more precise or accurate results.This is merely one example as to why a scalar may be applied to data.The scalar may be applied based upon known or estimated limitations ofthe user's equipment, environment, and/or other factors. For example, arun in a populated city like Chicago, New York, or Los Angeles may notbe as accurately captured with GPS sensors as if the user was conductingthe run in a less populated environment with less building or signalobstruction. Further, in those embodiments in which the user's activityis known, estimated, or inputted, the activity may be used to determinehow accurate or precise the resultant data is. Thus, either apredetermined scalar or a variable scalar may be used to augment oradjust data received from at least one source.

One or more normalizing factors configured to normalize data from the1st and/or 2nd source may be applied (e.g., block 808). In variousembodiments of the invention, normalizing factors may be used tocalculate an athletic metric, such as a cumulative energy expenditurevalue, which may be expressed as, for example, caloric expenditure.Based on one or more criterion, such as the classification of the useractivity, one or more normalizing factors (or processes utilizing one ormore factors) may be determined. For example, in one embodiment, atleast one of user's 124 activity may be classified (e.g., such as atblocks 603 and/or 706 of shown in FIGS. 6 and 7, respectively and one ormore corresponding normalizing factors applied to the athletic metric.In one example embodiment, normalizing factors may be applied toactivity classifications (or other variable(s)) to create a normalizedathletic metric, which may allow for an improved comparison of activitylevels of athletes and/or may promote collaboration among users,normalize results for competition among users of different capabilities,and otherwise encourage activity. In one embodiment, a normalized energyexpenditure value may be calculated using Equation 1 and/or Equation 2,each of which have been disclosed above, and, which may be calculatedmay be performed at a device that includes sensors that monitor activityand/or at another device that includes a processor, such as a mobilephone (see, e.g., 138) or server (see, e.g., 134).

As shown in equation 2, a scalar may be optionally implemented. Thescalar used in equation 2 may, but is not required to be, a wholenumber, such as 1, 3, 5 or 10. In some embodiments, the scalar isselected so that typical energy expenditure points fall within a desiredrange. The range of points may be desired for various games orcompetitions, and/or to provide a relation to a virtual currency orpoint system. Different and varying equations may be selected fordifferent games, activities, population dynamics, and competitions. Inone example a group may set handicaps among the players based onfitness, so that NEEVs may only be generated for those athletes that doa common activity or set of activities for longer period(s) of time. Agroup of users participating in an energy expenditure point competitionmay agree on a particular equation or method before beginning thecompetition. Yet, in other embodiments, NEEV calculation may not beexplicitly disclosed to one or more athletes or users so as to preventor attempt to prevent athletes from attempting to maximize points orotherwise “earn” values from conducting activities that exploit one ormore imprecisions in the system. As those skilled in the art willappreciate, every model will have limitations. And in this regard,aspects of this innovation attempt to minimize inaccurate and/orimprecise data values via one or more systems and methods disclosedherein. In some embodiments of the invention, a user may participate inmultiple competitions and earn different values for the same activitybecause of different calculation methods. For example, a user may beparticipating in two competitions that have unique calculation methods.The user may earn two different point totals for the two different gamesand a third point total for their overall energy expenditure. Some pointtotals may be maintained separate from an overall point total.

In those embodiments in which the athletic metric relates to energyexpenditure in which METs are utilized, some activities, games and/orcompetitions may limit awarding energy expenditure points for activitiesthat have relatively low MET values.

In some embodiments, awarding energy expenditure points for activitiesthat have relatively low MET values may also be limited all of the timeor in other situations. An optional step may be added to the processshown in FIG. 8 or other implementations to not award points foractivities that do not exceed a MET threshold value. For example, anexemplary threshold value may be 1.0, 2.0 or 3.0. In another embodiment,the threshold value may equal 2.8. Different games and competitions mayuse other threshold values. When the MET value does not exceed thethreshold, a step may be implemented to disregard the correspondingactivity and not use the activity when calculating energy expenditurepoints.

Another embodiment could have the threshold generally applied, but notwhen games or competitions are underway or at least certain games orcompetitions. The games or competitions may be based on all values. Inanother embodiment, a threshold may always apply even to games andcompetitions. In another embodiment, different thresholds may apply byactivity, game and/or competition, e.g., one for running briskly, onefor running, one for walking, and a default.

In certain implementations, such as after at least one of user's 124activity is classified according to one embodiment, one or moreprocesses may be implemented to determine a corresponding MET value. Forexample, the MET value may correspond to brisk running, running at amoderate pace, walking slowly or any other activity found inconventional MET tables. If an activity was not classified, such as inblock 603 and/or 706 of FIGS. 6-7, respectively, a default MET value maybe selected or derived. In some embodiment multiple default MET valuesmay be utilized. An activity's intensity, duration or othercharacteristic(s) may be assessed, from which one or more default METsmay be applied. These plural METs may be set via medians/averages,ranges, or other statistical approaches.

If the user's movement does not match an exercise defined by a METtable, the system 100 may identify one or more exercises that includemovements similar to the movement being performed by the user. Forexample, system 100 may determine that the user's lower body movessimilar to a squat and upper body moves similar to a pushup. System 100may calculate the number of calories the user would burn using theidentified MET tables as if the users were doing a squat, and as if theywere doing a pushup, as approximations for the amount of calories burnedby the user. In further embodiments, a new entry may be created. In thisregard, certain embodiments may permit the entry and lateridentification of new movements and/or exercises. In certainembodiments, the user may provide inputs regarding an approximatecaloric expenditure for an unidentified movement/exercise. Yet in otherembodiments, system 100 may calculate caloric expenditure, such as fromone or more sensors as discussed herein. In still yet furtherembodiments, system 100 may utilize one or more sensor readings as wellas an input from a user (and/or third-party) in determining attributes,such as caloric expenditure, for previously unknown movements orexercises. Examples of estimating caloric expenditure without METtables, may include but are not limited to, determining changes inpotential energy.

Alternative embodiments may use alternative or additional equations forcalculating point values and/or other quantities (e.g., part of block(s)808 or 810). The equations may include derivations of measured and/orcalculated values. Derivations that include time periods may be used toshow rates and rates of change. For example, one equation may be used todetermine a rate of accumulating activity points or energy expenditurepoints. Another equation may be used to determine a quantity of activitypoints or energy expenditure points accumulated over a predeterminedtime period.

Some equations may use variables other than time. For example, someequations may be used to calculate a value as a function of activityvalues or normalized energy expenditure values and/or steps. Calculatingvalues that are functions of activity values or NEEVs and othervariables may be used to compare the efficiencies of various activities.For example, an equation may be used to determine that taking steps at afaster pace may result in activity values or NEEVS accumulating at afaster per step pace. Another exemplary equation may determine activityvalues or NEEVs per a predetermined distance or a unit of distance.

Some equations may be used to calculate first and/or second derivativesof measured or calculated values to show rates and rates of change. Forexample, an equation may be used to calculate or estimate a rate ofaccumulation of activity points or energy expenditure points at a giventime. In some embodiments an instantaneous rate of accumulation ofactivity values or NEEVs may be displayed to a user via display 235 or adisplay that is part of a mobile device.

Before, during and/or after the normalized values are calculated, thecalculated values (raw, processed, or NEEVs) may be combined, such asbeing added, to a form a cumulative value (e.g., block 810, which maycalculate final cumulative normalized athletic metric for a first timeframe). The total may allow user 124 (and/or selected individuals orgroups approved by user 124) to see how many points are earned overvarious periods of time, such as days, weeks and months. Totals may alsobe calculated for multiple time periods. For example, a user may receivetotals for periods that include 24 hours, one week, one month and oneyear. In some embodiments users may select other time periods ordeselect time periods. A user may track multiple time periodsconcurrently and track points award since the beginning of use of adevice or start of a program. The total for any giving time period mayrepresent points earned for several activities. For example, in a day auser may receive points for walking, jogging and sprinting duringdifferent time periods. As mentioned above, the values earned for eachactivity may be a function of a corresponding activity factor.

FIG. 8B shows flowchart 811 of an example embodiment of calculating acumulative normalized athletic metric for a first time frame. Forexample, all or part of flowchart 811 of FIG. 8B may be performed aspart of block 810 of flowchart 800 (FIG. 8A). In one embodiment, anadjusted 1^(st) value may be derived from the first value; amodification scalar and a normalizing factor assigned to the first value(e.g., block 812). An adjusted 2^(nd) value may be derived from at leastthe second value (e.g., block 814) and a normalizing factor assigned tothe 2^(nd) value. Those skilled in the art will appreciate that theseare merely examples.

Certain embodiments may determine whether there is overlapping time inathletic movements representing the 1^(st) value and the 2^(nd) value(e.g., block 816). For example, as alluded to above, a collection ofsensors may be one or more devices, which in turn, may individuallyaggregate energy expenditure information. For example, a user may usesensors from two or more different vendors, where a first set of sensorsmay be configured to synchronize energy expenditure information with afirst device and a second set of sensors may be configured tosynchronize energy expenditure information with a second device. In suchcases, the energy expenditure information may be totaled separately onthe different devices. For example, the first device may store a totalenergy expenditure estimate of 5000 units gathered using the first setof sensors and the second device may store a total energy expenditureestimate of 6000 units gathered using the second set of sensors duringthe same or at least a portion of the same time. Unlike prior artmethods and systems, certain embodiments, may not merely use a defaultor the highest value. If it is determined that there is overlap, one ormore blocks may be implemented to determine or select the more accuratesource and proportionally attribute corresponding value (e.g., block818). Example systems may utilize one or more of the teachings of block920 of FIG. 9, block 1120 of FIG. 11, and/or other teachings herein.

In one embodiment, calculations may use the user's historical data,other user's historical data, and other sensory data (which may not beavailable to each other). For example, the second source may provide GPSdata which is not available to the first source or otherwise accountedfor in the 1^(st) source's data. Weather data may further be utilized,such as to provide confirmation or strength in the determination processthat the weather could have impacted GPS reception. Battery life mayalso alter the device(s) accuracy, therefore, may be an attributeconsidered which value is likely to be more accurate. However, unlikeprior art systems and methods any determination may be conducted after asource and/or activity scalar is applied (e.g., scalar from block 806).The process may occur after application of a normalizing factor to thesources under consideration. Thus, in certain embodiments, the adjustedvalues under consideration may be normalized and scaled before block818. As shown by way of block 820, the determination of 810 may befinalized using the proportional data for the overlapping time period.In certain embodiments, the final calculations, e.g., a cumulativemetric value, does not alter the values obtained to create or generatethe cumulative metric. For example, a user's data at the first and/orsecond source remains unaltered an intact. However, the cumulative datacan track a plurality of different disparate data sources.

FIG. 9 schematically-depicts one implementation of a process forcalculating energy expenditure for a user from data received from one ormore sources. As such, in one example, flowchart 900 of FIG. 9schematically-depicts a process for calculating an energy expenditure ofa user from data received from one or more devices utilizing differentoperating protocols. In one implementation, a first device may receivesensor data from a second connected device. As such, those of ordinaryskill in the art will recognize that any connection hardware, firmware,and/or software utilizing any communication protocol may be utilized tocommunicate information between the first device and the second device.In one example, the first device may be similar to one or more ofdevices 112, 126, 128, 130, and/or 400, as previously described.Furthermore, the second connected device may comprise one or more ofdevices 112, 126, 128, 130, and/or 400, and such that the secondconnected device may utilize a different operating protocol to the firstdevice. Further details of different operating protocols are describedin relation to FIG. 6. Accordingly, in one example, the first device mayreceive data from the second connected device at block 902 of flowchart900.

In one example, one or more processes may be executed to determinewhether received sensor data comprises of raw sensor data. Inparticular, raw sensor data may comprise unprocessed, or partiallyprocessed, output values from one or more sensor devices. In oneimplementation, raw sensor data may comprise numerical values generatedby one or more sensors from, among others, an accelerometer, agyroscope, a location-determining device (e.g., GPS), light sensor,temperature sensor (including ambient temperature and/or bodytemperature), heart rate monitor, image-capturing sensor, moisturesensor and/or combinations thereof. In particular, raw sensor data maybe utilized to calculate one or more activity metrics, and such that oneor more additional processes may be executed on raw data to arrive atone or more activity metrics, including, but not limited to, energyexpenditure, speed, distance, pace, power, and/or others. In oneexample, one or more processes may be executed to determine whetherreceived sensor data comprises raw sensor data at decision block 904.

In one example, if received sensor data is determined to comprise rawsensor data, flowchart 900 proceeds to decision block 906, wherein oneor more processes may be executed to determine whether the connecteddevice is recognized. In particular, and as previously described,flowchart 900 may be executed by a first device using data received froma second connected device. In one implementation, the data received fromthe second connected device may include information identifying thesecond connected device. As such, those of ordinary skill in the artwill recognize various identification methodologies that may be utilizedin this regard. For example, data packet headers may include one or moreunique identification codes identifying the second connected device, andthe like. As such, in one example, upon receipt of data from the secondconnected device, one or more processes may be executed to determinewhether the second connected device is known to the first device. In oneexample, these one or more processes executed to determine whether thesecond connected device is recognized may search through a database ofdevices with which the first device has communicated previously. Assuch, those of ordinary skill in the art will recognize various databaseconfigurations that may be utilized to store information related torecognized devices. In one example, server 111 may be utilized to storea database of such recognized devices, among others.

In one implementation, if, at decision block 904, the received data isnot determined to contain raw data, flowchart 900 proceeds to block 908.Accordingly, in one example, one or more processes may be executed toidentify or more activity metrics from received data. In this way, thereceived data may contain previously-processed information, such thatthe received data comprises one or more activity metrics including, butnot limited to, energy expenditure, speed, distance, pace, power, and/orothers.

Turning again to decision block 906, if, in one example, a connecteddevice is not recognized, flowchart 900 proceeds to block 910, wherein arequest may be sent from the first device to a second connected devicefor device information. As such, in one example, this request for deviceinformation may comprise a request for a unique identifier of the secondconnected device, and/or a network address of the second connecteddevice, among others. If, however, the connected device is recognized,flowchart 900 may proceed to block 912, wherein one or more activitymetrics may be calculated from the received data. As such, the one ormore activity metrics may be calculated based upon information stored inthe database related to the second connected device. For example, if itis determined that the second connected device comprises anaccelerometer, the raw data received may be identified as accelerationdata, and utilized to calculate one or more activity metrics appropriatefor data received from an accelerometer. As such, the database of deviceinformation may include formulae to be used to calculate one or moreactivity metrics based upon specific characteristics of a givenmonitoring device/sensor device. These specific characteristics mayinclude one or more accuracy metrics associated with a given device, orone or more characteristics of an operating protocol/manufacturerassociated with a given device. Furthermore, those of ordinary skill inthe art will recognize various activity metrics that may be calculatedfrom various raw data types, without departing from the scope of thedisclosures described herein. In another example, raw data from multipledifferent sources may be needed to calculate a given activity metric,among others. Accordingly, an activity metric, such as including, butnot limited to, energy expenditure, speed, distance, pace, power, and/orothers, may be calculated at block 912 of flowchart 900.

In one example, in response to a request for device information,flowchart 900 may proceed to decision block 914. As such, in oneimplementation, if no device information is received in response to thatrequest communicated at block 910, flowchart 900 may proceed to block916, and the received sensor data may be discarded. If, however, deviceinformation is received in response to the request made at block 910,flowchart 900 may proceed to block 918, and the requested deviceinformation may be added to the device database described in relation todecision block 906.

In one example, flowchart 900 may execute one or more processes todetermine whether an activity metric from a given source (i.e. a givensecond connected device) represents a best available source for saidspecific activity metric. For example, a user may be utilizing one ormore activity devices that may be capable of calculating a givenactivity metric from separate data sources (i.e. separate sensordevices). As such, in one implementation, flowchart 900 may execute oneor more processes to determine a best available source of a givenactivity metric. In one implementation, one or more processes may beexecuted to compare a received activity metric to a database of activitymetrics stored for a given user. Those of ordinary skill in the art willrecognize various different database methodologies that may be utilizedto store activity metrics for a user during a given activity periodwithout departing from the scope of the disclosures described herein. Assuch, decision block 920 may receive an activity metric, and search forthe same activity metric within an activity metric database. If it isdetermined that the activity metric is not already stored within thedatabase, decision block 920 may proceed to block 922 and add the metricto the database. Similarly, if it is determined that the activity metrichas already been stored within the database, but that activity metricstored within the database was calculated using data from a lessreliable/less accurate source, flowchart 900 may proceed to block 922,and replace the stored activity metric with the newly received activitymetric from a better source. If, however, at decision block 920 it isdetermined that the activity metric is not associated with a bestavailable source, flowchart 900 may proceed to block 924, and discardthe received data.

In response to the addition of the activity metric to the activitymetric database, flowchart 900 may proceed to block 926, and classifythe data into an activity group. As such, in one example, based upon oneor more stored activity metrics, an activity being performed by a usermay be categorized into a group of activities potentially beingperformed by the user. As such, this activity group may be a generalizedclassification comprising one or more activities that a user may beparticipating in. For example, an activity group may comprise one ormore activities classified based upon a speed detected for a user (e.g.a high-speed activity metric may result in an activity group comprisingcycling or sprinting, among others).

In one implementation, flowchart 900 may proceed to block 928 andcalculate an energy expenditure of the user based on one or more storedactivity metrics, and without identifying a specific activity beingperformed by a user. In particular, having classified received data intoan activity group at block 926, flowchart 900 may proceed to block 928and execute one or more processes to retrieve one or more formulae thatmay be utilized to calculate an energy expenditure, based upon one ormore of the stored activity metrics. In another implementation, anenergy expenditure of the user may be calculated following furtheractivity recognition processes. In particular, flowchart 900 may proceedfrom block 926 to block 930 and identify an activity metric not alreadystored in a metric database that would improve activity recognition(i.e. reduce a number of activities within the classified activity groupfrom block 926. In one specific example, block 930 may execute one ormore processes to identify activity recognition equipment/devices knownto be available to a user (e.g. activity devices owned by a user) thatmay be utilized to update one or more additional activity metrics notcurrently stored in a metric database for the user for a given activityperiod.

In one example, flowchart 900 may communicate a message to a user toutilize additional activity recognition equipment in order to improveactivity recognition. As such, this message may be communicated to theuser at block 932. In one example, the message may inform a user thatactivity recognition equipment previously known to be in the possessionof the user may be utilized to improve recognition of an activitycurrently being participated in by the user. In response, the user mayactivate one or more of the recommended additional activity recognitiondevices. As such, additional activity metrics may be received.Accordingly, if additional activity metrics are received, flowchart 900may precede from decision block 934 back to block 922. If, however, noadditional activity metric for received at block 934, flowchart 900 mayproceed to block 928.

In order to calculate an energy expenditure for a user from one or moreactivity metrics, one or more personal metrics for the user may beretrieved from a metric database. In one example, a database storing oneor more personal metrics for user may be a same database storing one ormore activity metrics calculated based upon an activity being performedby a user. However, those of ordinary skill in the art will recognizethat separate databases for the personal metrics and the activitymetrics may be utilized, without departing from the scope of thedisclosures described herein. In one example, one or more personalmetrics for a user may be retrieved at block 936, wherein the one ormore personal metrics may comprise one or more of sex, weigh, height,age, percent body fat, and/or other demographic or physiologicalparameters.

A normalized energy expenditure for a user may be calculated based uponone or more personal metrics and one or more activity metrics calculatedfrom data outputted by one or more activity devices. As such, those ofordinary skill in the art will recognize various formulae that may beutilized to calculate an energy expenditure based upon a combination ofactivity metrics and personal metrics, as previously described.Furthermore, calculation of this normalized energy expenditure may besimilar to the normalization processes described in relation to FIGS. 7Aand 7B.

FIG. 10 schematically-depicts another implementation of a process forcalculating an energy expenditure for a user from data received from oneor more sources. In one example, FIG. 10 may be utilized to calculate anenergy expenditure for a user based upon an activity classificationprovided by the user. As such, in one example, flowchart 1000 mayreceive an activity classification from a user at block 1002, and suchthat the received activity classification includes an identification ofan activity currently being performed by a user.

Separate to the activity classification received from the user at block1002, flowchart 1000 may receive data from a connected device at block1004. Accordingly, and as previously described in relation to FIG. 9,said connected device may be connected across any networktopology/methodology known to those of ordinary skill in the art,without departing from the scope of the disclosures described herein. Assuch, the connected device may include an activity recognition devicehaving one or more activity sensors, including, among others, anaccelerometer, a gyroscope, a location-determining device (e.g., GPS),light sensor, temperature sensor (including ambient temperature and/orbody temperature), heart rate monitor, image-capturing sensor, moisturesensor and/or combinations thereof. Furthermore, data received from aconnected device may be compared to one or more threshold values todetermine whether the received data is suitable for further processing.As such, and at decision block 1006, the received data may be comparedto one or more threshold values. If the received data is above the oneor more threshold values, flowchart 1000 may proceed to decision block1008. If however, the received data is not above one or more of thetested threshold values, flowchart 1000 may proceed to block 1010, andthe received data may be discarded.

In one implementation, one or more processes may be executed todetermine whether the data received at block 1004 has been received froma device utilizing a same operating protocol as a device into which thedata was received at block 1004. As such, in one example, one or moreprocesses may determine an operating protocol associated with a devicethat outputted the received data, at decision block 1008. These one ormore processes may identify an operating protocol associated with adevice that outputted the received data based upon, in one example,device identification information included in the received data. In oneimplementation, if it is found that the device that generated thereceived data utilizes a different operating protocol to the device thatreceived the data, flowchart 1000 may proceed to block 1012 As such, oneor more processes may be executed to condition the received data forcompatibility with the receiving operating protocol. In this way, one ormore processes may be executed to format or augment syntax of receiveddata for compatibility with an operating protocol associated with thereceiving device. These one or more processes may be executed at block1012, and may be similar to one or more conditioning processes discussedin relation to FIG. 6.

In one implementation, data received at block 1004 may comprise one ormore activity metrics. As such, and at block 1014 of flowchart 1000, oneor more processes may be executed to identify the received activitymetrics and add said activity metrics to a database, similar to theactivity metric database discussed in relation to FIG. 9. Further, basedupon the received activity data, including one or more activity metrics,one or more processes may be executed to classify the received data intoan activity group. This classification may be executed at block 1016, ormay be similar to those one or more processes executed at block 926 ofFIG. 9.

In one implementation, flowchart 1000 may calculate an energyexpenditure for a user based upon one or more stored activity metrics.As such, one or more processes may be executed to retrieve the one ormore stored activity metrics, in addition to one or more formulae thatmay be utilized to calculate an energy expenditure based upon theavailable activity metric information. As such, calculation of an energyexpenditure of the user based upon stored activity metrics may beexecuted at block 1018, and may be similar to block 928 from FIG. 9.

In order to calculate a normalized energy expenditure for a user, asdescribed in relation to FIGS. 7A and 7B, flowchart 1000 may retrieveone or more personal metrics for a user that are stored in a metricdatabase. As such, these personal metrics may include a user sex, weigh,height, age, percent body fat, and/or other demographic or physiologicalparameters. This retrieval of one or more personal metrics may beexecuted at block 1020, and may be similar to block 936 from FIG. 9.Accordingly, process 1000 may calculate a normalized energy expenditureat block 1022. In one example, the calculation of a normalized energyexpenditure at block 1022 may be similar to those processes executed atblock 938 of FIG. 9.

FIG. 11 schematically-depicts another implementation of a process forcalculation of an energy expenditure value for a user based uponreceived sensor data, as well as calculation of a confidence value to beassociated with a calculated energy expenditure. Accordingly, in oneimplementation, flowchart 1100 may receive data from a connected deviceat block 1102, and similar to block 902 from FIG. 9. In response,flowchart 1100 may execute one or more processes to determine whetherthe received data comprises raw data. In one implementation, these oneor more processes may be executed at decision block 1104, similar toblock 904 from FIG. 9. If it is determined that the received datacomprises raw data, flowchart 1100 may proceed to decision block 1106,and one or more processes may be executed to determine whether theconnected device is recognized by the receiving device. These one ormore processes executed to determine whether the connected device isrecognized by the receiving device may be similar to those processesexecuted at decision block 906 of FIG. 9.

If, in response to those processes executed at decision block 1104, itis determined that the received data does not contain raw data, one ormore further processes may be executed to identify one or more activitymetrics from the received data. These activity metric recognitionprocesses may be executed at block 1108, and may be similar to thoseprocesses executed at block 908 of FIG. 9.

Turning again to decision block 1106, if those processes executed atdecision block 1106 determine that the connected device is notrecognized, flowchart 1100 may proceed to block 1110. Accordingly, oneor more processes may be executed to send a request to the connecteddevice for device information at block 1110. As such, block 1110 may besimilar to block 910 from FIG. 9. If, however, the connected device isrecognized at decision block 1106, flowchart 1100 may proceed to block1112, and calculate an activity metric from the received data. In thisway, block 1112 may be similar to block 912 from FIG. 9.

If, in response to the request for device information communicated atblock 1110, device information is received from the connected device,flowchart 1110 may proceed from decision block 1114 to block 1118. Assuch, received device information may be added to a database at block1118. If, however, no device information is received in response to therequest is communicated at block 1110, the received sensor data may bediscarded at block 1116. In this way, decision block 1114 may be similarto block 914 from FIG. 9, and blocks 1116 and 1180 may be similar toblocks 916 and 918 from FIG. 9.

Decision block 1120 may execute one or more processes to determinewhether one or more activity metrics received from a connected device atblock 1102 represents one or more best available sources of saidactivity metrics. In this way, decision block 1120 may be similar todecision block 920 from FIG. 9. In turn, if it is determined that thereceived activity metrics do not represent a best available source ofdata, flowchart 1100 may proceed to block 1124, and that the receiveddata may be discarded. Conversely, if the one or more processes executedat decision block 1120 determine that the received activity metricsrepresent a best available data source, flowchart 1100 may proceed toblock 1122, and add the data to a metric database. In this way, block1122 may be similar to block 922 from FIG. 9. Furthermore, the receiveddata may be classified into an activity group, and such that an activitygroup may represent one or more activities that may be associated withthe data received at block 1102. This classification of data into anactivity group may be executed at block 1126, and may be similar tothose processes executed at block 926 of FIG. 9.

In one implementation, a confidence weighting may be associated with anactivity metric stored in a metric database. As such, a confidenceweighting may comprise a numerical value representing an accuracyassociated with one or more activity metrics. In one example, aconfidence weighting may be based upon a source of data used tocalculate a given activity metric. For example, a specific activitymetric may be calculated from sensor data received from multipledifferent sensor types, and such that a first sensor type may outputdata that is more accurate than a second sensor type. In turn, thespecific activity metric may be more accurate when calculated from dataoutputted from the first sensor type than data outputted from the secondsensor type, and the like. In this way, a confidence weighting may beassociated with an activity metric based upon one or more source devicesfrom which data was received in order to calculate said activity metric.

In another example, a confidence weighting may be based upon a specificactivity being performed by a user. For example, an activity metric maycomprise an energy expenditure value associated with the user. However,a confidence weighting associated with this energy expenditure activitymetric may take into account a specific activity being performed by auser. For example, if one or more activity recognition processesdetermine that the user is cycling, a first confidence weighting may beassociated with the activity metric. If, however, the one or moreactivity recognition processes determine that the user is running, asecond confidence weighting may be associated with the activity metric.In particular, the first confidence weighting may take into account thatan energy expenditure calculated for cycling is likely to be lessaccurate, given a specific activity recognition device, than the energyexpenditure calculated for running, given the same activity recognitiondevice. In one implementation, a confidence weighting may be associatedwith an activity metric stored in the metric database at block 1128. Inyet another example, a confidence weighting may take into account one ormore environmental conditions associated with a calculated activitymetric. As such, environmental conditions may include a signal-to-noiseratio associated with received sensor data, among others.

In one example, one or more processes to be executed to calculate anenergy expenditure of the user from one or more stored activity metrics,and such that the one or more stored activity metrics they comprise oneor more of a speed, a pace, an incline on which a user is moving, aheart rate, a metabolic equivalent value, a calorie consumption value, avolume of oxygen consumption, or a power value, among others. In thisway, an energy expenditure value may be calculated using one or moreformulae specific to a recognized activity and/or activity group withinwhich it is determined that the user is participating. As such, in oneexample, the calculation of an energy expenditure for a user based uponone or more stored activity metrics may be executed at block 1130 offlowchart 1100.

In one implementation, one or more processes may be executed to retrieveone or more confidence weightings associated with the activity metricsutilized in the calculation of the energy expenditure. In this way, oneor more confidence weightings based on, among others, a source of thedata received at block 1102, an activity/activity group identified atblock 1126, and/or one or more environmental conditions may be retrievedfrom a database, such as that database described in relation to block1128. In one example, retrieval of the one or more confidence weightingsmay be executed at block 1132. Accordingly, in one example, one or moreprocesses may be executed to calculate a confidence value for thecalculated energy expenditure for a user, and based upon the retrievedconfidence weightings, which are in turn related to the activity metricsutilized in the calculation of the energy expenditure. Those of ordinaryskill in the art will recognize various formulae that may be utilized tocalculate the confidence value associated with the calculator energyexpenditure, without departing from the scope of the disclosuresdescribed herein. As such, in one example, the one or more processesexecuted to calculate the confidence value may be associated with block1134.

FIG. 12 is a flowchart diagram of a process for publishing activityinformation to a social feed. In particular, flowchart 1200 may receivesensor data from a connected device. In this way, flowchart 1200 may beexecuted by a first device utilizing a first operating protocol, andsuch that the first device is in communication with a second deviceutilizing a second operating protocol. As such, in one example, sensordata may be received from the connected device at block 1202 offlowchart 1200, and such that blocks 1202 may be similar to block 1102from FIG. 11.

In one implementation, one or more processes may be executed totransform data received from the connected device. As such, these one ormore processes may transform the received data to extract one or moreactivity metrics, and the like. In one example, received data maycomprise raw data from which one or more activity metrics may becalculated. In another example, the received data may comprisepre-processed information that may be interpreted as one or moreactivity metrics. As such, these activity metrics may include, amongothers, speed, a pace, an incline on which a user is moving, a heartrate, a metabolic equivalent value, a calorie consumption value, avolume of oxygen consumption, or a power value. In one implementation,the received data may be transformed at block 1204.

In one example, flowchart 1200 may identify one or more activitiesassociated with the received sensor data. As such, in oneimplementation, an activity associated with the received sensor data maybe identified based upon one or more activity recognition processesexecuted on received sensor data, or based upon an explicitidentification of an activity communicated within the received sensordata (e.g. an activity identifier communicated within the receivedsensor data). As such, those of ordinary skill in the art will recognizevarious activity recognition processes that may be utilized in thisregard, without departing from the scope of the disclosures describedherein. Further, in one example, identification of one or moreactivities associated with received sensor data may be executed at block1206 of flowchart 1200.

In one example, flowchart 1200 may identify one or more socialconnections from a database, based upon the received sensor data and theone or more identified activities. As such, the one or more socialconnections may be associated with one or more social networks. Inparticular, the one or more social networks may include groups ofindividuals with common interests (e.g. athletic activity interests, andthe like). Further, the database from which the one or more socialconnections may be identified may be stored locally, such as one or moreof devices 112, 126, 128, 130, and/or 400, or may be a remote databasestored in a remote server, such as server 111, and the like. In thisway, one or more processes may be executed to identify one or moresocial connections based upon one or more specific activities beingperformed by the user. Accordingly, these one or more identificationprocesses may be executed at block 1208 of flowchart 1200.

In one example, a data feed, otherwise referred to as a feed, anelectronic message board, or communication channel may be utilized by auser in order to communicate information related to an activity beingperformed by a user. As such, in one implementation, the data feed maycommunicate athletic performance information to one or more socialconnections associated with user. In this way, a user may communicatereal-time information related to an activity being performed by theuser, and such that said one or more social connections may comparetheir own athletic performances to a real-time performance associatedwith the user. In one example, one or more processes may publishathletic performance information to a social feed associated with user,based upon the detected activity associated with block 1206, and thesocial connections identified at block 1208. In one example, thispublication may be executed at block 1210.

FIG. 13 is flowchart 1300 which shows an example implementation ofutilizing two different metrics from two different sources that werederived from two different processes. For example, in example decision1302, it may be determined whether a first metric (e.g., pace and/orenergy expenditure metric) obtained from a first source (such as the1^(st) source of block 802 of FIG. 8A) and a metric (e.g., energyexpenditure and/or pace metric) obtained from a second source (such asthe 2^(nd) source of block 804 of FIG. 8B). In one embodiment, pace maybe provided from the first source and the second source. In oneembodiment, the energy expenditure metric from multiple sources may beprovided from the same or similar processes (which may be separate fromthe calculation of pace). The first source may utilize a first processcomprising receiving locational data of the athlete during at least aportion of the first time frame, and based upon the locational data,determining a pace of the athlete during relevant portion of the firsttime frame. The second source may utilize a second process comprisingdetermining a quantity of steps taken by the athlete during at least aportion of the first time frame and based upon at least the quantity ofsteps determined, determining a pace of the athlete during the relevantportion of the first time frame.

Despite the 1^(st) and 2^(nd) sources being independent from each otherand not having access to each other's data, certain embodiments mayutilize either source's data and/or additional data. For example, in oneembodiment a pace metric may be combined with demographic informationstored on a database independent from either source to create a modifiedenergy expenditure metric (e.g., block 1304; if decision 1302 is in thenegative, however, block 1306 may be implemented to utilize the energyexpenditure metric in calculations. In one embodiment, a pace metricreceived from both of the first and second sources is combined withdemographic information located in a central database that isindependent from the first and second source to create a modified energyexpenditure metric.

In certain embodiments, the combination of the pace metrics with thedemographic information is part of the modification of the first processvalue from the value calculated from the first process based upon theidentity of at least one of the first source and the first device.

Block 1308 may be implemented to determine whether the modified energyexpenditure metric (e.g., determined at block 1304) or the receivedenergy expenditure more reliable or accurate. If it is, block 1310 maybe implemented in which the modified energy expenditure metric may beused for at least a portion of data from 1^(st) or 2^(nd) source incalculation. If not, block 1306 may be implemented to use the energyexpenditure metric for at least portion of data from 1^(st) or 2^(nd)source in subsequent calculations.

For the avoidance of doubt, the present application extends to thesubject-matter described in the following numbered paragraphs (referredto as “Para” or “Paras”):

-   -   1. A non-transitory computer-readable medium comprising        computer-executable instructions that when executed by a        processor are configured to perform at least:        -   receiving data from a connected device, wherein the            connected device utilizes a first operating system and the            processor utilizes a second operating system;            -   identifying an activity metric associated with the                received data;            -   storing the received data in the metric database;            -   classifying the received data into an activity group;            -   associating one or more stored activity metrics with one                or more respective confidence weightings, based on the                classified activity group;            -   calculating an energy expenditure value of a user from                the one or more stored activity metrics; and            -   calculating a confidence value to be associated with the                energy expenditure value, based on the one or more                confidence weightings.    -   2. The non-transitory computer-readable medium of Para 1,        further comprising, prior to storing the received data in the        metric database:        -   determining, by comparing the received data to a metric            database, whether the received data is a best available            source of data for the activity metric,        -   wherein if the received data is not a best available source            of data for the activity metric, discarding the received            data.    -   3. The non-transitory computer-readable medium of Para 1 or 2,        wherein the confidence value represents an accuracy of the        calculated energy expenditure value.    -   4. The non-transitory computer-readable medium of any preceding        Para, wherein a confidence weighting, from the one or more        confidence weightings, is associated with the activity metric        based on one or more characteristics of the connected device.    -   5. The non-transitory computer-readable medium of any preceding        Para, wherein a confidence weighting, from the one or more        confidence weightings, is associated with the activity metric        based on one or more environmental factors.    -   6. The non-transitory computer-readable medium of Para 5,        wherein the one or more environmental factors include a signal        to noise ratio of the received data.    -   7. The non-transitory computer-readable medium of any preceding        Para, wherein the received data is raw sensor data.    -   8. The non-transitory computer-readable medium of Para 7,        wherein the activity metric is calculated from the raw sensor        data by the processor.    -   9. The non-transitory computer-readable medium of any preceding        of Paras 1 to 6, wherein the received data comprises the        activity metric pre-calculated by the connected device.    -   10. The non-transitory computer-readable medium of any preceding        Para, wherein the activity group comprises one or more        activities that can be recognized based on one or more activity        metrics stored in the metric database.    -   11. The non-transitory computer-readable medium of Para 10,        wherein the computer-executable instructions, when executed by        the processor, are further configured to perform:        -   identifying an additional activity metric, not stored in the            metric database, to improve activity recognition by reducing            a number of activities in the activity group.    -   12. The non-transitory computer-readable medium of any preceding        Para, wherein in order to calculate the energy expenditure of        the user, the processor utilizes personal activity metrics        stored in the metric database, including: an age, a weight, a        height, and a gender of the user.    -   13. The non-transitory computer-readable medium of Para 12,        wherein the personal activity metrics include a resting heart        rate of the user.    -   14. The non-transitory computer-readable medium of Para 12 or        13, wherein in order to calculate the energy expenditure of the        user, the processor further utilizes performance activity        metrics that include a time over which an activity is being        performed by the user, and at least one performance activity        metric selected from: a speed, a gradient, a power, a heart        rate, a volume of oxygen consumption, a metabolic equivalent,        and a calorie count.    -   15. The non-transitory computer-readable medium of any of Paras        12 to 14, wherein the computer-executable instructions, when        executed by the processor, are further configured to perform:        -   normalizing the energy expenditure value of the user based            on the personal activity metrics.    -   16. An apparatus comprising:        -   a processor;        -   an input interface, configured to receive an activity            classification input from a user and sensor data from a            connected device, wherein the connected device utilizes a            first operating protocol and the input interface utilizes a            second operating protocol;        -   a memory storing computer-readable instructions that, when            executed by the processor, cause the apparatus to:            -   identify, based on the activity classification input                from the user, an activity being performed by the user;            -   identify an activity metric associated with the received                sensor data;                -   storing the received sensor data in the metric                    database;                -   classifying the received sensor data into an                    activity group;                -   associating one or more activity metrics stored in                    the metric database with one or more respective                    confidence weightings, based on the classified                    activity group;                -   calculating an energy expenditure of the user from                    the one or more stored activity metrics; and                -   calculating a confidence value to be associated with                    the energy expenditure value, based on the one or                    more confidence weightings.    -   17. The apparatus of Para 16, wherein the computer-readable        instructions, when executed by the processor, further cause the        apparatus to, prior to storing the received sensor data in the        metric database:        -   determine, by comparing the sensor received data to a metric            database, whether the received sensor data is a best            available source of data for the identified activity metric,            -   wherein if the received sensor data is not the best                available source of data for the identified activity                metric, discarding the received sensor data.    -   18. The apparatus of Para 17, wherein the computer-readable        instructions, when executed by the processor, further cause the        apparatus to:        -   determine whether the received data is a best available            source of data for the identified activity metric by            comparison of the received data to a threshold value.    -   19. The apparatus of any of Paras 16 to 18, wherein the        computer-readable instructions, when executed by the processor,        further cause the apparatus to:        -   condition the received data for compatibility with the            second operating protocol.    -   20. The apparatus of any of Paras 16 to 19, wherein the        connected device comprises a sensor selected from the group        consisting of a GPS, an accelerometer, a heart rate sensor, and        a gyroscope.    -   21. The apparatus of any of Paras 16 to 20, wherein in order to        calculate the energy expenditure of the user, the processor        utilizes personal activity metrics stored in the metric        database, including: an age, a weight, a height, and a gender of        the user.    -   22. The apparatus of Para 21, wherein the personal activity        metrics include a resting heart rate of the user.    -   23. A computer-implemented method of calculating a final        cumulative athletic metric based upon an athlete's athletic        movements during a first time frame that comprises at least a        first and a second time period, comprising:        -   receiving from a first source, a first value representing a            first athletic metric derived from a first process conducted            on a first device;        -   receiving from a second source that is independent of the            first source, a second value representing the first athletic            metric derived from a second process conducted on a second            device;        -   based upon an identity of at least one of the first source            and the first device, associating a modification scalar to            the first value;            -   assigning a normalizing factor configured to normalize                data received from the first source and the second                source;            -   calculating a final cumulative normalized athletic                metric for the first time frame using an adjusted first                value and adjusted second value, wherein the adjusted                first value is derived from the first value, the                modification scalar and the assigned normalizing factor,                and an adjusted second value is derived from at least                the second value and the assigned normalizing factor.    -   24. The method of Para 23, wherein the normalizing factor and        the modification scalar are each applied to the first value.    -   25. The method of Para 23 or 24, wherein one of the assigned        normalizing factor or the modification scalar is directly        applied to the first value to provide an interim first value,        and the other of the normalizing factor and the modification        scalar is applied to the interim first value.    -   26. The method of any of Paras 23 to 25, further comprising:        -   determining that the first value represents athletic            movements during a first time period within the first time            frame and the second value represents athletic movements            during a second time period within the first time frame; and    -   wherein the calculation of the final cumulative normalized        athletic metric comprises summing the adjusted first value and        the adjusted second value to create the final cumulative        normalized athletic metric.    -   27. The method of any of Paras 23 to 26, wherein the normalizing        factor is assigned to the first value based upon the activity        being performed by the athlete.    -   28. The method of any of Paras 23 to 27, further comprising:        -   determining that the first value represents athletic            movements during a first time period within the first time            frame and the second value represents athletic movements            during a second time period within the first time frame,            wherein at least a portion of the first time period and the            second time period overlap;        -   and wherein the calculating the final normalized cumulative            athletic metric comprises:    -   determining that the first source better represents the athletic        movements, and in response, proportionally attributing the first        value to the overlap.    -   29. The method of any of Paras 23 to 28, wherein the first        athletic metric comprises an energy expenditure metric.    -   30. The method of any of Paras 23 to 29, wherein the athletic        metric comprises a pace metric.    -   31. The method of Para 30, wherein the first process comprises:        -   receiving locational data of the athlete during at least a            portion of the first time frame; and        -   based upon the locational data, determining a pace of the            athlete during relevant portion of the first time frame; and        -   wherein the second process comprises:            -   determining a quantity of steps taken by the athlete                during at least a portion of the first time frame; and            -   based upon at least the quantity of steps determined,                determining a pace of the athlete during the relevant                portion of the first time frame.    -   32. The method of Para 30 or 31, further comprising:        -   combining the pace metric received from both of the first            and second sources with demographic information located in a            central database that is independent from the first and            second source to create an energy expenditure metric.    -   33. The method of Para 32, wherein the combination of the pace        metrics with the demographic information is part of the        modification of the first process value from the value        calculated from the first process based upon the identity of at        least one of the first source and the first device.    -   34. The method of Para 33, wherein athletic metric comprises        both an energy expenditure metric and a pace metric, the method        further comprising:        -   combining the pace metric received from both of the first            and second sources with demographic information stored on a            central database that is independent from the first and            second source to create a modified energy expenditure            metric; and        -   determining whether to use the energy expenditure metric or            the modified energy expenditure.    -   35. The method of Para 33 or 34, wherein a first time period and        a second time period of the first time frame overlap.

CONCLUSION

-   -   Aspects of the embodiments have been described in terms of        illustrative embodiments thereof. Numerous other embodiments,        modifications and variations within the scope and spirit of the        appended Clauses will occur to persons of ordinary skill in the        art from a review of this disclosure. For example, one of        ordinary skill in the art will appreciate that the steps        illustrated in the illustrative figures may be performed in        other than the recited order, and that one or more steps        illustrated may be optional in accordance with aspects of the        embodiments.

We claim:
 1. An athletic system comprising: a processor; a display; anda non-transitory computer-readable medium comprising computer-executableinstructions that when executed by the processor are configured to causethe processor to at least: receive athletic data from a connecteddevice, wherein the connected device utilizes a first operating systemand the processor utilizes a second operating system; identify anactivity metric associated with the received data; determine with theprocessor, by comparing the received data to a metric database, whetherthe received data is a best available source of data for the activitymetric, wherein if the received data is not a best available source ofdata for the activity metric, discarding the received data, and whereinif the received data is the best available source of data for theactivity metric: store the received data in the metric database;classify the received data into an athletic activity group; associateone or more stored activity metrics with one or more respectiveconfidence weightings, based on the classified activity group; calculatean energy expenditure value of a user from the one or more storedactivity metrics; calculate a confidence value to be associated with theenergy expenditure value, based on the one or more confidenceweightings; and display the calculated energy value of the user on thedisplay.
 2. The athletic system of claim 1, the computer readable mediumfurther comprising: an input interface configured to receive an activityclassification input from a user and sensor data from the connecteddevice.
 3. The athletic system of claim 1, wherein the confidence valuerepresents an accuracy of the calculated energy expenditure value. 4.The athletic system of claim 1, wherein a first confidence weighting,from the one or more confidence weightings, is associated with theactivity metric based on one or more characteristics of the connecteddevice.
 5. The athletic system of claim 1, wherein a first confidenceweighting, from the one or more confidence weightings, is associatedwith the activity metric based on one or more environmental factors. 6.The athletic system of claim 5, wherein the one or more environmentalfactors include a signal to noise ratio of the received data.
 7. Theathletic system of claim 1, wherein the received data is raw sensordata.
 8. The athletic system of claim 7, wherein the activity metric iscalculated from the raw sensor data by the processor.
 9. The athleticsystem of claim 1, wherein the received data comprises the activitymetric pre-calculated by the connected device.
 10. The athletic systemof claim 1, wherein the activity group comprises one or more activitiesthat can be recognized based on one or more activity metrics stored inthe metric database.
 11. The athletic system of claim 10, wherein thecomputer-readable medium further comprises computer-executableinstructions, when executed by the processor, are further configured tocause the processor to perform: identifying an additional activitymetric, not stored in the metric database, to improve activityrecognition by reducing a number of activities in the activity group.12. The athletic system of claim 1, wherein calculating the energyexpenditure of the user comprises: processing personal activity metricsstored in the metric database, including at least two of: an age, aweight, a height, and a gender of the user.
 13. The athletic system ofclaim 12, wherein calculating the energy expenditure of the usercomprises utilizing a personal activity metric stored in the metricdatabase, wherein the personal activity metrics include at least aresting heart rate of the user.
 14. The athletic system of claim 12,wherein calculating the energy expenditure of the user comprisesutilizes performance activity metrics that include a time over which anactivity is being performed by the user, and at least one performanceactivity metric selected from: a speed, a gradient, a power, a heartrate, a volume of oxygen consumption, a metabolic equivalent, and acalorie count.
 15. The athletic system of claim 14, wherein thecomputer-readable medium further comprises computer-executableinstructions, that when executed by the processor, cause the processorto perform at least: normalizing the energy expenditure value of theuser based on the personal activity metrics.
 16. A computer-implementedmethod of calculating a final cumulative athletic metric based upon anathlete's athletic movements during a first time frame that comprises atleast a first and a second time period, comprising: electronicallyreceiving from a first source, a first value representing a firstathletic metric derived from a first process conducted on a firstdevice; electronically receiving from a second source that isindependent of the first source, a second value representing the firstathletic metric derived from a second process, independent of the firstprocess, conducted on a second device; based upon an identity of atleast one of the first source and the first device, associating amodification scalar to the first value; assigning a normalizing factorconfigured to normalize data received from the first source and thesecond source; and calculating a final cumulative normalized athleticmetric for the first time frame using an adjusted first value andadjusted second value, wherein the adjusted first value is derived fromthe first value, the modification scalar and the assigned normalizingfactor, and an adjusted second value is derived from at least the secondvalue and the assigned normalizing factor.
 17. The method of claim 16,wherein the normalizing factor and the modification scalar are eachapplied to the first value.
 18. The method of claim 16, wherein one ofthe assigned normalizing factor or the modification scalar is directlyapplied to the first value to provide an interim first value, and theother of the normalizing factor and the modification scalar is appliedto the interim first value.
 19. The method of claim 16, furthercomprising: determining that the first value represents athleticmovements during a first time period within the first time frame and thesecond value represents athletic movements during a second time periodwithin the first time frame; and wherein the calculation of the finalcumulative normalized athletic metric comprises summing the adjustedfirst value and the adjusted second value to create the final cumulativenormalized athletic metric.
 20. The method of claim 16, wherein thenormalizing factor is assigned to the first value based upon an activitybeing performed by the athlete.